Faculty of Engineering,
Computer Science and
Psychology
Module Handbook
Master’s Course
Cognitive Systems
(Study and Examination Regulations 06.06.2014)
Winter Term 2015
Based on Rev. 1639. Last changed 12.10.2015 at 07:36 by vpollex. Generated 16.10.2015 at 13:11 o’clock.
Contents
1 Basic Subjects
1.1 Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1.1 Introduction to Cognitive Psychology for Non-Psychologists . . . . .
1.1.2 Introduction to Experimental Methods for Non-Psychologists . . . . .
1.2 Computer Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.1 Formal Foundations of Computer Science . . . . . . . . . . . . . . .
1.2.2 Fundamentals of Interactive Systems – Design, Analysis, and Usability
1.2.3 Introduction to Computer Science for Psychologists . . . . . . . . . .
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2 Core Subjects
2.1 Cognitive Systems I – Concepts, Modeling, Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Cognitive Systems II – Higher-Order Cognitive Competencies . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Cycle of Lectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3 Special Subjects
3.1 Algorithms for Knowledge Representation . . . . . . . . . . . . . . . .
3.2 Big Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Computergrafik I - Grundlegende Konzepte . . . . . . . . . . . . . . . .
3.4 Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6 Decision Making and User Experience in Human-Technology-Interaction
3.7 Dialogue Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.8 Einführung in die Künstliche Intelligenz . . . . . . . . . . . . . . . . . .
3.9 Foundations of Semantic Web Technologies . . . . . . . . . . . . . . .
3.10 Hands On: Mobile Assessment of Biosignals - Principles and Application
3.11 Information Processing in Neural Systems . . . . . . . . . . . . . . . .
3.12 Information Retrieval and Web Mining . . . . . . . . . . . . . . . . . .
3.13 Intelligente Handlungsplanung . . . . . . . . . . . . . . . . . . . . . .
3.14 Introduction to Data Science . . . . . . . . . . . . . . . . . . . . . . .
3.15 Mobile and Ubiquitous Computing . . . . . . . . . . . . . . . . . . . .
3.16 Self Regulation: Development, Neuro-Cognition and Psychopathology .
3.17 Specialization in Cognitive Psychology . . . . . . . . . . . . . . . . . .
3.18 Usability Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.19 Vision in Man and Machine . . . . . . . . . . . . . . . . . . . . . . . .
4 Applied Subjects
4.1 Cognitive Vision . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Project Cognitive Vision - Algorithms and Applications .
4.1.2 Project Computational Vision and Image Processing . .
4.1.3 Project and Seminar Visual Information Processing . . .
4.1.4 Vision . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.5 Vision in Man and Machine (in Applied Subject) . . . .
4.2 Visual Computing . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1 Data Visualization . . . . . . . . . . . . . . . . . . . . .
4.2.2 Visual Computing . . . . . . . . . . . . . . . . . . . . .
4.3 Pattern Recognition . . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 Algorithms for Emotion Recognition in Human Computer
4.3.2 Neural Networks (in Applied Subject) . . . . . . . . . .
4.3.3 Pattern Recognition . . . . . . . . . . . . . . . . . . . .
4.3.4 Pattern Recognition and Machine Learning Algorithms .
4.3.5 Reinforcement Learning . . . . . . . . . . . . . . . . . .
4.4 Cognitive Ergonomics . . . . . . . . . . . . . . . . . . . . . . .
4.4.1 Instructional Design and Technology . . . . . . . . . . .
4.4.2 Project - Driver-Vehicle Interaction . . . . . . . . . . . .
4.4.3 Project Cognitive Ergonomics . . . . . . . . . . . . . . .
4.4.4 Psychology of Automation . . . . . . . . . . . . . . . .
4.4.5 Transportation Human Factors . . . . . . . . . . . . . .
4.5 Data Science . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1 Big Data Analytics . . . . . . . . . . . . . . . . . . . .
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4.6
4.7
4.8
4.5.2 Business Process Intelligence . . . . . . . . . .
4.5.3 Data Mining . . . . . . . . . . . . . . . . . . .
4.5.4 Information Retrieval and Web Mining . . . . .
4.5.5 Introduction to Data Science . . . . . . . . . .
4.5.6 Project Non-Traditional Database Architectures
4.5.7 Research Trends in Data Science . . . . . . . .
User-Centered Planning . . . . . . . . . . . . . . . . .
4.6.1 Project User-Centered Planning . . . . . . . . .
Semantic Web Technology . . . . . . . . . . . . . . .
4.7.1 Project Semantic Web Technologies . . . . . .
Cognitive Smart Systems . . . . . . . . . . . . . . . .
4.8.1 Cognitive Smart Systems . . . . . . . . . . . .
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5 Interdisciplinary Subjects
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5.1 Human-Computer Dialogue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.1.1 Mobile Mensch-Computer-Interaktion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.1.2 Project - Design, Implementation and Evaluation of Dialogue Systems . . . . . . . . . . . . . . . . 110
5.1.3 Psychology of Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5.1.4 Transportation Human Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.2 Cognitive Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
5.2.1 Cognitive Modelling for Computer Scientists and Psychologists . . . . . . . . . . . . . . . . . . . . 115
5.2.2 Computational Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.2.3 Project Cognitive Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.3 Cognitive Neuroscience - Experimental and Modeling Perspectives . . . . . . . . . . . . . . . . . . . . . . 121
5.3.1 Body and Mind: philosophisch–wissenschaftstheoretische Grundlagen der Cognitive and Neuro Sciences121
5.3.2 Cognitive and Neural Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.3.3 Project Psychophysical investigation of functions in perception, cognition and motor behavior . . . 125
5.3.4 Project and Seminar Visual Information Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 127
5.3.5 Psychophysics - Methods, Paradigms, and Experimentation . . . . . . . . . . . . . . . . . . . . . . 129
5.3.6 Seminar Computational modeling of cognitive functions . . . . . . . . . . . . . . . . . . . . . . . . 131
5.3.7 Thinking about Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
5.3.8 Topics in Cognitive Neuroscience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
5.4 Cognitive Agents, Companions, and Cognitive Apps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.4.1 Cognitive Agents, Companions, and Mobile Apps in Healthcare . . . . . . . . . . . . . . . . . . . . 136
5.4.2 Project Cognitive Solutions for Mobile Guidance, Assessment and Crowd Sensing . . . . . . . . . . 138
5.4.3 Project Inreasing Patient Engagement through Cognitive Companions and Apps . . . . . . . . . . . 140
6 Final Thesis
142
6.1 Master’s Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
3
1 Basic Subjects
1.1 Psychology
1.1.1 Introduction to Cognitive Psychology for Non-Psychologists
Token / Number:
88csyneu
German title:
Einführung in die Kognitionspsychologie für Nichtpsychologen
Credits:
8 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Anke Huckauf
Training staff:
Teaching staff from the Institute of Psychology and Pedagogy
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Foundation Subject Psychology for Computer Scientists
Requirements
(contentual):
None
Learning objectives:
The students can orient themselves in the sub-disciplines of psychology. They
learn about basic phenomena, paradigms and concepts in all fields of cognitive
psychology, which include psychophysics, perception, attention, consciousness,
learning, memory, problem solving, language, emotion, motivation, and volition.
Students know the empirical foundations of each research field.
Content:
-
Psychophysics
Perception and attention
Learning and memory
Language
Motivation, volition, and emotion
Literature:
Will be announced at the beginning of the course.
Basis for:
This module provides the basis for all psychological modules. It also helps for
any of the interdisciplinary topics that are scheduled later in the curriculum.
Modes of learning
and teaching:
Lecture Introduction to experimental psychology: Perception and cognition, 2
SWS (N.N.)
Lecture Introduction to experimental psychology: Language, emotion, motivation, and volition, 2 SWS (N.N.)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 180 h
Sum: 240 h
Course assessment
and exams:
The exams are oral or written depending on the number of participants. The
exact mode is announced in the lectures.
Requirements
(formal):
Bachelor in computer Science or related field
Grading:
The mark for the module is determined by the mean exam mark in both courses.
4
Basierend auf Rev. 1573. Letzte Änderung am 07.10.2015 um 22:59 durch mreichert.
5
1.1.2 Introduction to Experimental Methods for Non-Psychologists
Token / Number:
88csyneu
German title:
Einführung in experimentelles Arbeiten für Nichtpsychologen
Credits:
10 ECTS
Semester hours:
7
Language:
Englisch
Turn / Duration:
Every Winter Term / 2 Semester
Module authority:
Prof. Dr. Anke Huckauf
Training staff:
Teaching staff from the Insitute of Psychology and Pedagogy
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Foundation Subject Psychology for Computer Scientists
Requirements
(contentual):
None
Learning objectives:
The students can distinguish between various basic and advanced experimental
designs related to cognitive psychology. They can further demonstrate practical research skills through their participation in research projects. The students
know how to properly design and execute experimental studies. They understand and are able to apply basic descriptive and inferential statistical methods.
Finally, students are able to select appropriate experimental methods including
psychophysical approaches for certain research questions.
Content:
Theoretical bases of a quantitative approach to experimental research designs.
Practical aspects of the sample, procedure, and analysis of experimental data
collections in the laboratory and in applied settings.
- Designing empirical studies
- Correlative and experimental set-ups
- Descriptive and inferential statistics
Literature:
Will be announced at the beginning of the course
Basis for:
This module provides the basis for all psychological modules. It also helps for
any of the interdisciplinary topics that are scheduled later in the curriculum.
Modes of learning
and teaching:
Lecture Psychological Research Methods, 3 SWS, winter term (N.N.)
Exercise Psychological Research Methods, 2 SWS, winter term (N.N.)
Lecture Psychological Research Methods, 2 SWS, summer term (N.N.)
Estimation of
effort:
Active Time: 100 h
Preparation and Evaluation: 300 h
Sum: 400 h
Course assessment
and exams:
The exam is oral or written depending on the number of participants. The
exact mode is announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the mark in the two exams. A grade
bonus according to §13 (5) of the study and exam regulations cognitive systems
is given if the exercises are passed successfully.
6
Basierend auf Rev. 1577. Letzte Änderung am 08.10.2015 um 08:27 durch vpollex.
7
1.2 Computer Science
1.2.1 Formal Foundations of Computer Science
Token / Number:
88csyneu
German title:
Formale Grundlagen der Informatik
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Uwe Schöning
Training staff:
Prof.
Prof.
Prof.
Prof.
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Foundation Subject Computer Science for Psychologists
Requirements
(contentual):
Introduction to Computer Science for Psychologists
Learning objectives:
The students are familiar and able to work with common mathematical and
computer science formalisms describing sets, sequences, alphabets, and words.
They are able to use these methods adequately. Furthermore, they are familiar
with the use of formal grammars, automata, and graphs for modelling. Finally,
they understand the concept of computability and its limits.
Dr.
Dr.
Dr.
Dr.
Uwe Schöning
Jacobo Torán
Enno Ohlebusch
Günther Palm
Content:
- Formal concepts: sets, sequences, functions, relations, words, and alphabets.
- Formal grammars, languages, and automata as well as their properties and
relations. Chomsky-hierarchy.
Literature:
- Lecture Notes
- Sipser: Introduction to the Theory of Computation. Course Technology, 3rd
edition, 2012
- Hopcroft, Motwani, Ullman: Introduction to Automata Theory, Languages, and
Computation. Prentice Hall, 3rd edition, 2006
- Schöning, Kestler: Mathe-Toolbox. Lehmanns Media, 2. erw. Auflage, 2011
- Schöning: Theoretische Informatik - kurz gefasst. 5. Auflage, Spektrum, 2008
- Wegener: Theoretische Informatik. Teubner, 1993
- Blum: Einführung in Formale Sprachen, Berechenbarkeit, Informations- und
Lerntheorie. Oldenburg, 2007
Basis for:
The module helps for any of the interdisciplinary topics that are scheduled later
in the curriculum.
Modes of learning
and teaching:
Lecture Formal Foundations, 3 SWS (Prof. Dr. Uwe Schöning, Prof. Dr. Jacobo
Torán, Prof. Dr. Enno Ohlebusch, Prof. Dr. Günther Palm)
Exercise Formal Foundations, 1 SWS ()
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
8
Course assessment
and exams:
The exam is oral or written depending on the number of participants. The
exact mode is announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade bonus
according to §13 (5) of the study and exam regulations cognitive systems is
given if the exercise class is passed successfully.
Basierend auf Rev. 1602. Letzte Änderung am 08.10.2015 um 16:00 durch mreichert.
9
1.2.2 Fundamentals of Interactive Systems – Design, Analysis, and Usability
Token / Number:
88csyneu
German title:
Grundlagen Interaktiver Systeme – Design, Analyse und Usability
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Heiko Neumann
Prof. Dr. Enrico Rukzio
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Foundation Subject Computer Science for Psychologists
Requirements
(contentual):
None
Learning objectives:
The students acquire elementary concepts and methods for the design of interactive systems, particularly software systems. They can develop new solutions for
the design of user interfaces and evaluate existing solutions for this. The necessary skills are developed on the basis of investigations of human perceptual and
cognitive capabilities, generations of different interaction metaphors and the
underlying techniques, as well as (formal) methods for the development and
analysis of mechanisms in interaction and related mechanistic cognitive models.
Content:
-
Introduction to HCI
Perceptual and cognitive aspects of HCI
Cognitive processes and interaction
Empirical issues in the design of interactive systems
The interface – input and output devices in HCI
Interface design – dialog notations and interface formats
Dialog notation – graphs, nets and graphical formats
Interface design and cognitive modeling
Literature:
The following suggested literature is a selected sample as reference. Specific
links to literature will be given in the beginning of the lecture.
- A. Dix, J. Finlay, G.Abowd, R. Beale: Human-Computer Interaction. PrenticeHall, 1998
- J. Raskin: The Humane Interface. Addison-Wesley, 2000
- D. Benyon: Designing Interactive Systems. Pearson Education Ltd, 2010
Basis for:
The module helps for any of the interdisciplinary topics that are scheduled later
in the curriculum.
Modes of learning
and teaching:
Lecture Fundamentals of Interactive Systems – Design, Analysis, and Usability,
2 SWS (Prof. Dr. Heiko Neumann, Prof. Dr. Michael Weber)
Exercise Fundamentals of Interactive Systems – Design, Analysis, and Usability,
2 SWS (Prof. Dr. Heiko Neumann, Prof. Dr. Michael Weber, Nikolas Grottendieck)
10
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
The exam is oral or written depending on the number of participants. The
exact mode is announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade bonus
according to §13 (5) of the study and exam regulations cognitive systems is
given if the exercise class is passed successfully.
Basierend auf Rev. 1568. Letzte Änderung am 07.10.2015 um 22:39 durch mreichert.
11
1.2.3 Introduction to Computer Science for Psychologists
Token / Number:
88csyneu
German title:
Einführung in die Informatik für Psychologen
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Jun.-Prof. Dr. Birte Glimm
Training staff:
Jun.-Prof. Dr. Birte Glimm
Prof. Dr. Thom Frühwirth
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Foundation Subject Computer Science for Psychologists
Requirements
(contentual):
None
Learning objectives:
The students can describe the elementary concepts and methods of computer
science. They can develop algorithms for small problems in a programming language using simple control structures (loops, conditionals). The students can
list the basic data structures (arrays, lists, trees, graphs) and judge their suitability for certain situations. They can explain basic computational mechanisms
and programming paradigms (imperative-, object oriented, and functional programming, modularisation, Divide-and-Conquer, iteration, recursion). Finally,
they can name, describe, and apply standard algorithms for searching and sorting.
Content:
-
Definition and properties of algorithms
Number representations
Elementary concepts, principles and methods of computer science
Principles of computing components
Principles of operating systems
Principles of programming languages (syntax, semantics, elementary datatypes,
data- and control structures)
Literature:
- Nell Dale and John Lewis. Computer Science Illuminated. Jones & Bartlett
Learning. 2014. 5th edition. ISBN 978-1-4496-7284-3
Basis for:
The module is the basis for the lecture “Formal Concepts and Foundations of
Computer Science”. The module also helps for any of the interdisciplinary topics
that are scheduled later in the curriculum.
Modes of learning
and teaching:
Lecture Introduction to Computer Science for Psychologists, 2 SWS ()
Exercise Introduction to Computer Science for Psychologists, 2 SWS ()
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
The exam is oral or written depending on the number of participants. The
exact mode is announced in the lecture.
12
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade bonus
according to §13 (5) of the study and exam regulations cognitive systems is
given if the exercise class is passed successfully.
Basierend auf Rev. 1577. Letzte Änderung am 08.10.2015 um 08:27 durch vpollex.
13
2 Core Subjects
2.1 Cognitive Systems I – Concepts, Modeling, Perception
Token / Number:
88csy72405
German title:
Kognitive Systeme I – Konzepte, Modellierung, Perzeption
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof.
Prof.
Prof.
Prof.
Prof.
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Core Subject
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Requirements
(contentual):
None
Learning objectives:
The students can describe the core research topics of cognitive systems and
apply the fundamental methodological concepts including experimental investigations. They can further list research and application-related questions concerning the different topics of cognitive systems research from different perspectives (i.e., empirical, modeling, and technical). Even further, the students can
discuss specific aspects of detailed structure and function of cognitive systems
and develop solutions for different sample questions.
Content:
Dr.
Dr.
Dr.
Dr.
Dr.
Anke Huckauf
Markus Kiefer
Heiko Neumann
Günther Palm
Olga Pollatos
The course program provides a first rigorous overview of topics introducing the
field of Cognitive Systems. The topics are organized along the following areas
and a general presentation of methodological and technical core principles:
- Fundamentals - introduction and models
- Basic methodology and technology
- Perception and cognition
14
Literature:
-
The following suggested literature is a selected sample as reference. Specific
links to literature will be given in the beginning of the lecture.
M.W. Eysenck, M.T. Keane: Cognitive Psychology: A Student’s Handbook,
6th ed. Taylor & Francis Ltd, 2010
J. Ward: The Student’s Guide to Cognitive Neuroscience. Psychology Press,
2006
R Sun (ed.): The Cambridge Handbook of Computational Psychology. Cambridge Univ. Press, 2008
P.S. Churchland, T.J. Sejnowski: The Computational Brain. MIT Press, 1999
S.E. Palmer: Vision Science - Photons to Phenomenology. MIT Press, 1999
S. Russell, P. Norvig: Artificial Intelligence - A Modern Approach, 3rd Ed.,
Prentice-Hall, 2010
R. Morris, L. Tarassenko, M. Kenward: Cognitive Systems - Information Processing meets Brain Science, Elsevier, 2006
Basis for:
–
Modes of learning
and teaching:
Lecture Cognitive Systems – Concepts, Modeling, Perception, 3 SWS (members
of the teaching staff)
Exercise Cognitive Systems – Concepts, Modeling, Perception, 1 SWS (members of the teaching staff)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h.
Sum: 180 h
Course assessment
and exams:
The exams are oral or written depending on the number of participants. The
exact modes are announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade bonus
according to §13 (5) of the study and exam regulations in cognitive systems is
given if the exercise class is passed successfully.
Basierend auf Rev. 1601. Letzte Änderung am 08.10.2015 um 15:57 durch mreichert.
15
2.2 Cognitive Systems II – Higher-Order Cognitive Competencies
Token / Number:
88csy72406
German title:
Kognitive Systeme II – Höhere kognitive Fähigkeiten
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Martin Baumann
Dr. Jenny Bittner
Prof. Dr. Susanne Biundo-Stephan
Jun.-Prof. Dr. Birte Glimm
Prof. Dr. Anke Huckauf
Prof. Dr. Wolfgang Minker
Prof. Dr. Günther Palm
Prof. Dr. Enrico Rukzio
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Core Subject
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Requirements
(contentual):
The course builds upon the fundamental knowledge about cognitive systems,
in particular, regarding cognitive models, basic methodology and technology,
and perception and cognition as taught in the course “Cognitive Systems I –
Concepts, Modeling, Perception”.
Learning objectives:
The students can describe the core research topics of cognitive systems and
apply the fundamental methodological concepts including experimental investigations. They can list research and application-related questions concerning
the different topics of cognitive systems research from different perspectives,
namely empirical, modeling, and technical. Even further, the students can discuss specific aspects of detailed structure and function of cognitive systems and
develop solutions for different sample questions.
Content:
The course program provides a first rigorous overview of topics introducing the
field of Cognitive Systems. The topics are organized along the following areas
with a focus on higher-order cognitive function:
- Learning and instruction
- Planning, decision-making & working memory
- Interaction and communication
16
Literature:
-
The following suggested literature is a selected sample as reference. Specific
links to literature will be given in the beginning of the lecture.
M.W. Eysenck, M.T. Keane: Cognitive Psychology: A Student’s Handbook,
6th ed. Taylor & Francis Ltd, 2010
J. Ward: The Student’s Guide to Cognitive Neuroscience. Psychology Press,
2006
R Sun (ed.): The Cambridge Handbook of Computational Psychology. Cambridge Univ. Press, 2008
P.S. Churchland, T.J. Sejnowski: The Computational Brain. MIT Press, 1999
S.E. Palmer: Vision Science - Photons to Phenomenology. MIT Press, 1999
S. Russell, P. Norvig: Artificial Intelligence - A Modern Approach, 3rd Ed.,
Prentice-Hall, 2010
R. Morris, L. Tarassenko, M. Kenward: Cognitive Systems - Information Processing meets Brain Science, Elsevier, 2006
Basis for:
–
Modes of learning
and teaching:
Lecture Cognitive Systems II – Higher-Order Cognitive Competencies, 3 SWS
(members of the teaching staff)
Exercise Cognitive Systems II – Higher-Order Cognitive Competencies, 1 SWS
(members of the teaching staff)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h.
Sum: 180 h
Course assessment
and exams:
The exams are oral or written depending on the number of participants. The
exact modes are announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade bonus
according to §13 (5) of the study and exam regulations in cognitive systems is
given if the exercise class is passed successfully.
Basierend auf Rev. 1572. Letzte Änderung am 07.10.2015 um 22:56 durch mreichert.
17
2.3 Cycle of Lectures
Token / Number:
88csyneu
German title:
Ringvorlesung
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Martin Baumann
Prof. Dr. Susanne Biundo-Stephan
Prof. Dr. Thom Frühwirth
Jun.-Prof. Dr. Birte Glimm
Prof. Dr. Anke Huckauf
Prof. Dr. Markus Kiefer
Prof. Dr. Klaus Melchers
Prof. Dr. Wolfgang Minker
Prof. Dr. Heiko Neumann
Prof. Dr. Günther Palm
Prof. Dr. Harald C. Traue
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Core Subject
Requirements
(contentual):
None
Learning objectives:
The students can describe the core research topics of cognitive systems and
specific aspects of their relation to basic science, technology and application.
They can list research and application-related questions concerning the different
topics from various perspectives. The students can present selected topics in
the field of cognitive systems and discuss their impact.
Content:
Several lectures are presented that cover active research topics in the field of
Cognitive Systems. A mentorium is run in parallel to these lectures supported by
student mentors. They help guiding the participants during the semester with
regard to the course program and presentations. Students meet with the lecturers after his/her presentation to discuss questions regarding the lecture but
also to get information about related aspects regarding the master’s program
in Cognitive Systems. Students are required to select a topic of their specific
interest and get in touch with the particular lecturer to ask for additional literature. In a final poster presentation the students present their selected topic.
Literature:
Specific links to the literature regarding the lectures will be given by the lecturers.
The following list serves as a generic overview to gather the scope of the research
field:
- C. Forsythe, M.L. Bernard, T.E. Goldsmith (eds.): Cognitive Systems - Human
Cognitive Models in Systems Design. Lawrence Erlbaum Assoc., Publ., 2006
- R. Morris, L. Tarassenko, M. Kenward: Cognitive Systems: Information Processing Meets Brain Science. Academic Press, 2005
- J. Ward: The Student’s Guide to Cognitive Neuroscience. Psychology Press,
2006
18
Basis for:
The module helps for any of the interdisciplinary topics that are scheduled later
in the curriculum.
Modes of learning
and teaching:
Lecture Cycle of Lectures, 2 SWS (members of the teaching staff)
Exercise Mentorium, 2 SWS (members of the teaching staff, mentors)
Estimation of
effort:
Active Time: 45 h
Preparation and Evaluation: 135 h
Sum: 180 h
Course assessment
and exams:
All participants will actively participate in a poster presentation at the end of
the term which is worth a certificate of successful attendance in this course.
The posters will be graded by the lecturers. The participant is required to
select a particular topic in Cognitive Science, as presented during this course.
He/she will generate and present a poster at the end of the period of lectures
in the winter term (February). During the preparation the candidate gets in
touch with one particular lecturer of the program (candidate’s selection) to ask
for literature and specific background material.
Requirements
(formal):
None
Grading:
Basierend auf Rev. 1576. Letzte Änderung am 08.10.2015 um 08:09 durch mreichert.
19
3 Special Subjects
3.1 Algorithms for Knowledge Representation
Token / Number:
88csy71815
German title:
Algorithmen in der Wissensrepräsentation
English title:
Algorithms for Knowledge Representation
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Jun.-Prof. Dr. Birte Glimm
Training staff:
Dr. Yevgeny Kazakov
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Theoretische und Mathematische
Methoden der Informatik
Software-Engineering, M.Sc., Core Subject, Theoretische und Mathematische
Methoden der Informatik
Computer Science, M.Sc., Specialization Subject, Intelligente Systeme
Computer Science, M.Sc., Specialization Subject, Theoretische Informatik
Computer Science and Media, M.Sc., Core Subject, Theoretische und Mathematische Methoden der Informatik
Computer Science and Media, M.Sc., Specialization Subject, Intelligente Systeme
Computer Science and Media, M.Sc., Specialization Subject, Theoretische Informatik
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Basic programming skills and a good understanding of the development of
algorithms. Basic knowledge of logic and automated reasoning from as taught
in the lectures “Einführung in die Künstliche Intelligenz”, “Formale Grundlagen”
or “Foundations of Semantic Web Technologies” are helpful but not required.
Learning objectives:
The students can specify knowledge in a formal way using First-Order and
Description Logics. They can name the expressivity of the Description Logics
used and estimate the resulting (theoretical) complexity of algorithms dor the
typical reasoning problems. The students can compute entailed consequences
of a knowledge base using different inference procedures. They can explain
the advantages and disadvantages of the different algorithms and compare the
algorithms with each other, e.g., regarding their worst-case or average-case
complexity. The students can generalise the formal proofs of correctness, which
allows them to obtain results also for different logic fragments.
20
Content:
- Introduction to Description Logics as fragments of First-Order Logic
- Properties od logics (finite model property, compactness, tree model property,
...)
- Goals of automated deduction in knowledge representation (satisfiability, classification, entailment, . . . )
- Procedures for automated reasoning (tableau, hypertableau, resolution,
consequence-based reasoning, automata)
- Proof procedures (correctness and termination of the algorithms)
- complexity of the algorithms
Literature:
- Franz Baader, Diego Calvanese, Deborah L. McGuinness, Daniele Nardi, Peter
F. Patel-Schneider. The Description Logic Handbook: Theory, Implementation
and Applications. Cambridge University Press. 2007. 2-te Auflage. ISBN
978-0521876254
- Uwe Schöning. Logik für Informatiker. Spektrum. ISBN 3-8274-1005-3
- Melvin Fitting. First-Order Logic and Automated Theorem Proving. Springer.
ISBN 0-387-94593-8
- John Kelly. The Essence of Logic. Prentice Hall. ISBN 0-13-396375-6
Basis for:
Master’s thesis in the area of intelligent systems and automated reasoning.
Modes of learning
and teaching:
Lecture Algorithms for Knowledge Representation, 3 SWS (Dr. Yevgeny Kazakov)
Exercise Algorithms for Knowledge Representation, 1 SWS (Dr. Yevgeny Kazakov)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
The exam is oral or written depending on the number of participants. The
exact mode is announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade
bonus according to §13 (5) of the study and exam regulations Informatik/Medieninformatik/Software Engineering/Cognitive Systems is given if
the exercise class is passed successfully.
Basierend auf Rev. 1422. Letzte Änderung am 09.04.2015 um 19:39 durch bglimm.
21
3.2 Big Data Analytics
Token / Number:
88csyneu
English title:
Big Data Analytics
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Martin Theobald
Training staff:
Prof. Dr. Martin Theobald
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Informationssysteme
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Software-Engineering, M.Sc., Specialization Subject, Informationssysteme
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Computer Science and Media, M.Sc., Specialization Subject, Informationssysteme
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Application Subject Data science
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Grundkenntnisse zu “Datenbanken und Informationssytemen” und “Data Science”, wie sie beispielsweise in der Vorlesung Einführung in Data Science vermittelt werden, sind von Vorteil.
Learning objectives:
Der Kurs vermittelt den Studierenden einen detaillierten Einblick in die Funktionsweise und die theoretischen Grundlagen zur skalierbaren Analyse und verteilten
Verarbeitung von großen Datenmengen. Die Studierenden erkennen, welche
Datenstrukturen und Algorithmen der verteilten Analyse von großen Datenmengen zu Grunde liegen. Des Weiteren sind die Studierenden in der Lage, komplexe
Anwendungen mittels dieser Ansätze zu realisieren.
22
Content:
-
-
Literature:
Die Mastervorlesung Big Data Analytics vertieft die Grundkenntnisse, welche
die Studierenden im Bachelorstudiengang in den Bereichen “Datenbanken und
Informationssysteme” sowie “Data Science” erlangt haben. Die Vorlesung geht
eingehend auf die Funktionsweisen und die theoretischen Grundlagen verteilter
Informationssysteme sowie der verteilten Datenanalyse ein. Die Vorlesung beginnt mit den statistischen Grundlagen zur verteilten Datenverarbeitung und
legt insbesondere einen Fokus auf die Ausführung von verteilten Datenbankoperationen im gesamten Spektrum von “Create, Read, Update, Delete” (CRUD)
mittels klassischer SQL und aktueller NoSQL-Architekturen.
Synchronisationsverfahren, Recovery sowie Client-Server und Client-Client Architekturen von verteilten Dateisystemen in Apache Hadoop und MapReduce.
Hauptspeicherorientierte, verteilte Datenverarbeitung in Apache Spark.
Exakte, parallele Ausführung von traditionellen Transaktionsmodellen wie
“Atomicity, Consistency, Isolation und Durabiltiy” (ACID) sowie relaxierte Varianten mit eventueller Konsistenz (“CAP Theorem”)
Verteilungs- und Partitionierungsstrategien für große Datenmengen (“Sharding”)
mit MapReduce
Skalierbare Ausführung von analytischen Anfragen im Bereich OLAP und OLTP
Verteilte Graphdatenbanksysteme
Weitere Anwendungen im Bereich des Maschinellen Lernens und der Visualisierung von großen Datenmengen
Zusammenfassend bietet die Vorlesung Big Data Analytics einen detaillierten
Einblick in die oben genannten Technologien und zeigt diese in ihrem Zusammenspiel. Im Gegensatz zum Bachelorkurs “Einführung in Data Science” liegt
hier der Schwerpunkt in den theoretischen Grundlagen und statistischen Methoden, die der Datenverarbeitung in verteilten Informationssystemen zu Grunde
liegen.
- Vorlesungsskript
Basis for:
Der Kurs bietet eine ideale Basis für weitere Projekt- und Masterarbeiten in
den Bereichen “Datenbanken und Informationssysteme” sowie “Data Science”,
welche von DBIS angeboten werden.
Modes of learning
and teaching:
Lecture Big Data Analytics, 3 SWS (Prof. Martin Theobald)
Exercise Big Data Analytics, 1 SWS ()
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung erfolgt in Form einer schriftlichen Klausur. Bei geringer
Teilnahme erfolgt die Modulprüfung ggf. mündlich; dies wird zu Beginn der
Veranstaltung bekannt gegeben.
Requirements
(formal):
Keine.
Grading:
Die Modulnote ergibt aus der Modulprüfung.
Bei einer erfolgreichen Teilnahme an den Übungen wird dem Studierenden
ein Notenbonus auf die Modulprüfung bis zur nächst besseren Zwischenstufe
gewährt. Eine Notenverbesserung von 5,0 auf 4,0 ist nicht möglich (§13 Absatz
4 der Fachspezifischen Studien- und Prüfungsordnung für die Bachelor- und
Masterstudiengänge Informatik und Medieninformatik). Die genauen Modalitäten werden zu Beginn der Veranstaltung bekannt gegeben.
Basierend auf Rev. 1624. Letzte Änderung am 09.10.2015 um 17:05 durch mreichert.
23
3.3 Computergrafik I - Grundlegende Konzepte
Token / Number:
88csy72408
English title:
Computer Graphics I - Basic Concepts
Credits:
6 ECTS
Semester hours:
4
Language:
Deutsch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Timo Ropinski
Training staff:
Prof. Dr. Timo Ropinski
Integration of module
into courses of studies:
Computer Science, B.Sc., Main Subject,
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, B.Sc., Main Subject,
Computer Science and Media, M.Sc., Core Subject, Mediale Informatik
Software-Engineering, B.Sc., Main Subject,
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Keine
Learning objectives:
Die Studierenden kennen grundlegende Konzepte und Algorithmen aus dem
Bereich Computergrafik und können diese in ihren eigenen Grafikanwendungen
umsetzen. Dabei sind sie in der Lage, polygonale Modelle unter Anwendung
von Texturierung und Beleuchtung zur Anzeige zu bringen. Weiterhin kennen
Sie die konzeptionellen Stufen der Renderingpipeline als grundlegende Schritte
der Bildsynthese, und könne häufig verwendete Grafikalgorithmen auf der CPU
und der GPU umsetzen.
Content:
Der Kurs behandelt die grundlegenden Konzepte der Computergrafik, wobei
ein Schwerpunkt auf Echtzeit-fähiger Grafik liegt, wie sie beispielsweise in Computerspielen zum Einsatz kommt. Im Zentrum steht die Renderingpipeline,
als konzeptionelle Grundlage für moderne Bildsynthese Systeme. Die thematisierten Algorithmen werden zunächst in der Vorlesung theoretisch bearbeitet,
bevor eine Auswahl in den Übungen praktisch umgesetzt wird. Die praktische
Umsetzung erfolgt in C/C++ in Kombination mit dem Grafikstandard OpenGL,
wobei am Anfang der Übungen eine Einführung in C/C++ gegeben wird. Im
Rahmen des Kurses werden die folgenden Themen behandelt:
Ray Tracing
Grafikprogrammierung in OpenGL
Geometrische Transformationen und Projektionen
Beleuchtungsberechnung
Clipping Algorithmen
Rasterisierung und Texturierung
Geometrisches Modellieren
ILIAS:
–
24
Literature:
- P. Shirley, M. Ashikhmin, S. Marschner: Fundamentals of Computer Graphics,
AK Peters.
- D. Shreiner, G. Sellers, J. Kessenich, B. Licea-Kane: OpenGL Programming
Guide: The Official Guide to Learning OpenGL, Addison-Wesley.
Basis for:
–
Modes of learning
and teaching:
Lecture Computergrafik I, 3 SWS (Prof. Dr. Timo Ropinski)
Exercise Computergrafik I, 1 SWS (N.N)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung, welche die Inhalte aus der Vorlesung und den Übungen
abdeckt, erfolgt schriftlich.
Requirements
(formal):
Keine
Grading:
Die Modulnote ergibt sich aus der Modulprüfung. Bei einer erfolgreichen
Teilnahme an den Übungen wird dem Studierenden ein Notenbonus auf die
Modulprüfung bis zur nächst besseren Zwischenstufe gewährt. Eine Notenverbesserung von 5,0 auf 4,0 ist nicht möglich (§13 Absatz 4 der Fachspezifischen Studien- und Prüfungsordnung für die Bachelor- und Masterstudiengänge
Informatik und Medieninformatik). Die genauen Modalitäten werden zu Beginn
der Veranstaltung bekannt gegeben.
Basierend auf Rev. 1583. Letzte Änderung am 08.10.2015 um 12:08 durch mreichert.
25
3.4 Data Mining
Token / Number:
88csy71994
English title:
Data Mining
Credits:
6 ECTS
Semester hours:
4
Language:
Deutsch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Dr. Friedhelm Schwenker
Training staff:
Dr. Friedhelm Schwenker
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Neuroinformatik
Computer Science and Media, M.Sc., Specialization Subject, Neuroinformatik
Computer Science, M.Sc., Specialization Subject, Mustererkennung
Computer Science and Media, M.Sc., Specialization Subject, Mustererkennung
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Specialization Subject
Cognitive Systems, M.Sc., Application Subject Data science
Requirements
(contentual):
Grundkenntnisse in Neuroinformatik
Learning objectives:
Die Studierenden kennen die wesentlichen Methoden und Verfahren des Data
Mining. Sie kennen die grundlegenden Methoden der uni-variaten und multivariaten Statistik und sind speziell mit den maschinellen Lernverfahren des Data
Mining zur Clusteranalyse, Klassifikation und Regression vertraut und können
diese in kleineren Aufgabenstellungen auch anwenden.
Content:
-
Uni- und multivariate statistische Verfahren
Clusteranalyseverfahren
Visualisierung und Dimensionsreduktion
Lernen von Assoziationsregeln
Klassifikationverfahren
Regrossion und Prognose
Statistische Evaluierung
Literature:
- Mitchell, Tom: Machine Learning, Mc Graw Hill, 1997
- Bishop, Chris: Pattern Recognition and Machine Learning, Springer, 2007
- Hand, David und Mannila, Heikki und Smyth, Padhraic: Principles of Data
Mining, MIT Press, 2001
- Witten, Ian H. und Frank, Eibe: Data mining, Morgan Kaufmann, 2000
- Skript zur Vorlesung, 2011
Basis for:
–
Modes of learning
and teaching:
Lecture Data Mining, 2 SWS (Dr. Friedhelm Schwenker)
Exercise Data Mining, 2 SWS (Dr. Friedhelm Schwenker)
26
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung erfolgt schriftlich.
Requirements
(formal):
Bachelor
Grading:
Die Modulnote ergibt sich aus der Modulprüfung. Bei erfolgreicher Teilnahme
an den Übungen wird dem Studierenden ein Notenbonus gemäß §13 (5) der
fachspezifischen Prüfungsordnung Informatik/Medieninformatik/Software Engineering gewährt.
Basierend auf Rev. 1563. Letzte Änderung am 07.08.2015 um 07:58 durch vpollex.
27
3.5 Data Visualization
Token / Number:
88csy??????
English title:
Data Visualization
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Timo Ropinski
Training staff:
Prof. Dr. Timo Ropinski
Integration of module
into courses of studies:
Computer Science, B.Sc., Main Subject,
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, B.Sc., Main Subject,
Computer Science and Media, M.Sc., Core Subject, Mediale Informatik
Software-Engineering, B.Sc., Main Subject,
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Cognitive Systems, M.Sc., Specialization Subject
Cognitive Systems, M.Sc., Application Subject Visual Computing
Computer Science, Lehramt, Optional Module,
Requirements
(contentual):
Grundlegende Kenntnisse in der Computergrafik werden vorausgesetzt.
Learning objectives:
Die Studierenden kennen grundlegende Konzepte und Algorithmen aus dem
Bereich Visualisierung und können diese anwenden. Sie sind in der Lage, abstrakte und räumliche Daten so zu visualisieren, dass gewünschte Zusammenhänge klar verständlich werden. Des Weiteren können sie ein breites Spektrum
an Visualisierungstechniken technisch umsetzen, oder sofern in Anwendungen
verfügbar, erfolgreich anwenden.
Content:
Es werden die Grundlagen aus verschiedenen Bereichen der Visualisierung
vermittelt. Dabei werden die bearbeiteten Techniken in den Kontext der
Visualisierungs-Pipeline eingeordnet, welche als roter Faden für das Modul gilt.
Der Hauptfokus liegt dabei auf interaktiven Visualisierungstechniken, welche es
dem Benutzer erlauben mit den Visualisierungen zu interagieren, um beispielsweise die darzustellenden Daten zu filtern oder Darstellungsparameter zu verändern. Es werden die folgenden Themen behandelt:
Einordnung der Teilgebiete
Die Visualisierungs Pipeline
Datenstrukturen für räumliche Daten
Visualisierung von Skalar-, Vektor und Tensor-Feldern
Visualisierung Multi-Parametrischer Daten
Glyph-basierte Techniken
Ausgewählte Aspekte der visuellen Wahrnehmung
Anwendung moderner Visualisierungs Systeme
ILIAS:
–
28
Literature:
-
Es existiert kein Lehrbuch, welches alle behandelten Aspekte abdeckt. Daher
wird spezielle Literatur zu den einzelnen Kapiteln in der Vorlesung angegeben.
Als übergreifende Werke sind die folgenden Bücher zu nennen:
Matt Ward, Georges Grinstein, Daniel Keim: Interactive Data Visualization –
Foundations, Techniques, and Applications, CRC Press 2010.
Tamara Munzner: Visualization Analysis and Design, AK Peters 2014.
Colin Ware: Information Visualization: Perception for Design, Morgan Kaufmann 2012.
Alexandru C. Telea: Data Visualization: Principles and Practice, AK Peters
2014.
Basis for:
–
Modes of learning
and teaching:
Lecture Visualisierung, 3 SWS (Prof. Dr. Timo Ropinski)
Exercise Visualisierung, 1 SWS (Robin Skånberg)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung, welche die Inhalte aus der Vorlesung und den Übungen
abdeckt, erfolgt schriftlich oder mündlich.
Requirements
(formal):
Grading:
Die Modulnote ergibt sich aus der Modulprüfung. Bei einer erfolgreichen
Teilnahme an den Übungen wird dem Studierenden ein Notenbonus auf die
Modulprüfung bis zur nächst besseren Zwischenstufe gewährt. Eine Notenverbesserung von 5,0 auf 4,0 ist nicht möglich (§13 Absatz 4 der Fachspezifischen Studien- und Prüfungsordnung für die Bachelor- und Masterstudiengänge
Informatik und Medieninformatik). Die genauen Modalitäten werden zu Beginn
der Veranstaltung bekannt gegeben.
Basierend auf Rev. 1544. Letzte Änderung am 16.06.2015 um 14:01 durch vpollex.
29
3.6 Decision Making and User Experience in Human-Technology-Interaction
Token / Number:
88csyneu
German title:
Entscheidungen und User Experience in der Mensch-Technik-Interaktion
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Sporadic (Summer Term2015) / 1 Semester
Module authority:
Prof. Dr. Martin Baumann
Training staff:
Prof. Dr. Jenny Bittner
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Learning objectives:
Content:
- Psychology for non-Psychologists or Computer Science for Psychologists (1st
semester courses)
- Cognitive Systems – Concepts, Terminology, Methods
The students can distinguish between various basic and advanced theories in the
fields of decision making and user experience. The students know how to design and execute empirical studies in this area of Human-Technology-Interaction.
They are able to apply basic descriptive and inferential statistical methods. Students demonstrate empirical skills by participating in a research-related project.
Theoretical bases of research designs in the fields of decision making and user
experience. Practical aspects of the data collection in the laboratory and in
applied technological settings.
- Decision making
- Motivation & Emotion
- Communication & Interaction
Literature:
Will be announced at the beginning of the course
Basis for:
Cognitive Systems, M.Sc., Specialisation Subjects Psychology
Modes of learning
and teaching:
Lecture Decision
Interaction, 2 SWS
Exercise Decision
Interaction, 2 SWS
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Written report. The exact mode is announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the mark in the exam. A grade
bonus according to §13 (5) of the study and exam regulations cognitive systems
is given if the exercises are passed successfully.
Making and User Experience in Human-Technology(Prof. Dr. Jenny Bittner)
Making and User Experience in Human-Technology(Prof. Dr. Jenny Bittner)
30
Basierend auf Rev. 1528. Letzte Änderung am 10.06.2015 um 13:49 durch hneumann.
31
3.7 Dialogue Systems
Token / Number:
88csy70423
German title:
-
Credits:
6 ECTS
Semester hours:
4
Language:
deutsch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr.-Ing. Dr. Wolfgang Minker
Training staff:
Prof. Dr.-Ing. Dr. Wolfgang Minker
Integration of module
into courses of studies:
Electrical Engineering, M.Sc., Elective Module, Engineering Sciences
Communications Technology, M.Sc., Optional Technical Module, Communications Engineering
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Bachelor. No prerequisites from other lectures required. Some basic knowledge
in digital signal processing, computer science, cybernetics and statistics would
be helpful.
Learning objectives:
The student should:
- achieve a general theoretical knowldege in the domain of multimodal spoken
natural language dialogue technology,
- understand the interdisciplinary character of the field,
- achieve some practical knowledge through exercises with real systems and their
components at different levels of processing.
Content:
The lecture provides an introduction into the area of multimodal spoken natural
language dialogue systems. A particular focus is placed on acoustic processing,
speech signal analysis, recognition, spoken natural language understanding, dialogue processing and speech synthesis. The topics will be illustrated throughout
practical sessions and demonstrations of applications and products. Local companies working in the field of multimodal spoken natural language dialogue
systems will provide guest lectures.
Topics:
- Human Communication. Speech communication, structure and properties of
speech, speech production, and speech perception.
- Spoken Natural Language Dialogue Systems Overview. Disciplines of speech
processing, history, speech coding, speech synthesis, speech recognition, speech
identification/verification, semantic analysis, dialogue modelling.
32
Content (continued):
- Speech Synthesis. Relationship between phonetics and written language, speech
synthesis steps, phonetic inventory, speech signal production, speech synthesis
(concatenation), linear prediction, prosody control.
- Acoustic Processing. Beamforming, spectral subtraction, noise reduction, echo
compensation, GSM-coding, blind source separation.
- Speech Recognition. Overview over the most commonly used techniques in
speech recognition, such as feature extraction from speech, statistical modelling
of speech, search and speaker adaptation techniques.
- Semantic Analysis and Dialogue Modelling. Theory of formal languages, Chomsky hierarchy, word problem, finite automata, parsing, syntactic vs. semantic
grammars, rule-based vs. statistical approaches to semantic analysis, dialogue
modelling and application control.
- Systems Evaluation, Applications and Products. Evaluation of speech recognition and spoken language dialogue systems, overview on research projects,
commercially available and research prototypes.Exercises and Practicals. Spoken natural language dialogue systems development with a focus on parsing
and VoiceXML based dialogue management.
Literature:
- copies of slides
- J. Allen: Natural Language Understanding , The Benjamin/Cummings Publishing Company, Inc., 1988
- W. Minker, A. Waibel and J. Mariani: Stochastically-based semantic analysis ,
The Kluwer International Series in Engineering and Computer Science, Kluwer
Academic Publishers, Boston, 1999
- L.R. Rabiner and B.H. Juang: An introduction to Hidden Markov Models ,
IEEE Transactions on Acoustics: Speech and Signal Processing , 3:1, pp. 4-16,
1986
- S.J. Young, P.C. Woodland, and W. Byrne: Spontaneous Speech Recognition
for the Credit Card Corpus Using the HTK Toolkit , IEEE Trans. Speech and
Audio Processing, Vol 2, No 4, 1994
Basis for:
keine Angaben
Modes of learning
and teaching:
Lecture “Dialogue Systems”, 2 SWS ()
Exercise “Dialogue Systems”, 2 SWS ()
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 60 h
Self-Study: 60 h
Sum: 180 h
Course assessment
and exams:
Teilnahme an den Vorlesungen und Übungen. In der Regel mündliche Prüfung,
ansonsten schriftliche 90 minütige Prüfung.
Requirements
(formal):
Voraussetzung für die Prüfungszulassung ist der Erwerb eines Übungsscheins,
welcher die erfolgreiche Teilnahme an den Übungen bestätigt.
Grading:
an Hand des Prüfungsergebnisses
Basierend auf Rev. 1393. Letzte Änderung am 08.01.2015 um 11:05 durch hneumann.
33
3.8 Einführung in die Künstliche Intelligenz
Token / Number:
88csy70329
English title:
Introduction to Artificial Intelligence
Credits:
6 ECTS
Semester hours:
4
Language:
Deutsch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Susanne Biundo-Stephan
Training staff:
Prof. Dr. Susanne Biundo-Stephan
Integration of module
into courses of studies:
Computer Science, B.Sc., Main Subject,
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, B.Sc., Main Subject,
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Software-Engineering, B.Sc., Main Subject, Software-Engineering
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Grundkenntnisse in praktischer und theoretischer Informatik
Learning objectives:
Die Studierenden kennen grundlegende Vorgehensweisen und Methoden der künstlichen Intelligenz. Sie sind mit den wichtigsten Problemlösungsverfahren vertraut, können diese implementieren und kennen deren formale Eigenschaften.
Sie sind in der Lage zu beurteilen, für welche Problemstellungen welche
dieser Verfahren geeignet sind. Die Studierenden kennen die wichtigsten Wissensrepräsentationsformalismen und können informelle Problembeschreibungen
entsprechend formalisieren. Darüber hinaus sind sie in der Lage, den Einsatz von
Methoden und Verfahren der künstlichen Intelligenz bei der Entwicklung komplexer Anwendungssysteme gezielt zu bewerten, zu planen und durchzuführen.
Content:
-
Intelligente Agenten
Problemlösen durch Suche
Informierte und Constraint-basierte Suche
Spiele als Suchprobleme
Aussagen- und Prädikatenlogik
Automatisches Beweisen durch Resolution
Grundlagen der Wissensrepräsentation und -modellierung
Handlungsplanung: lineare und nicht-lineare Verfahren
Symbolische Lernverfahren
Literature:
- S. Russell, P. Norvig: Artificial Intelligence - A Modern Approach, 2. Auflage,
Prentice-Hall, 2003
- Deutsche Übersetzung: S. Russell, P. Norvig: Künstliche Intelligenz. Ein moderner Ansatz, 2. Auflage, Pearson Studium, 2004
- Chr. Beierle, G. Kern-Isberner: Methoden wissensbasierter Systeme, 2. Auflage,
Vieweg, 2003
- N. J. Nilsson: Artificial Intelligence: A New Synthesis, Morgan Kaufmann, 1998
34
Basis for:
Bachelor- und Masterarbeiten im Bereich Künstliche Intelligenz / Intelligente
Systeme
Modes of learning
and teaching:
Lecture Einführung in die Künstliche Intelligenz, 2 SWS (Prof. Dr. Susanne
Biundo-Stephan)
Exercise Einführung in die Künstliche Intelligenz, 2 SWS (diverse Mitarbeiter)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung erfolgt schriftlich. Im Rahmen der Übungen wird der Lernfortschritt überprüft. Studierende, die 50% der Übungsaufgaben erfolgreich
bearbeitet haben, erhalten in der Modulprüfung einen Notenbonus von 0.3.
Requirements
(formal):
Keine
Grading:
Die Modulnote ergibt sich aus der Modulprüfung. Bei erfolgreicher Teilnahme
an den Übungen wird dem Studierenden ein Notenbonus gemäß §13 (5) der
fachspezifischen Prüfungsordnung Informatik/Medieninformatik/Software Engineering gewährt.
Basierend auf Rev. 1512. Letzte Änderung am 03.06.2015 um 08:23 durch vpollex.
35
3.9 Foundations of Semantic Web Technologies
Token / Number:
88csy71806
German title:
Semantic Web Grundlagen
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Jun.-Prof. Dr. Birte Glimm
Training staff:
Jun.-Prof. Dr. Birte Glimm
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Intelligente Systeme
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Computer Science and Media, M.Sc., Specialization Subject, Intelligente Systeme
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Basic knowledge of the World Wide Web as taught in the BSc course Web
Engineering; basic knowledge in logics and automated reasoning such as taught
in the course Introduction to Artificial Intelligence (Einführung in die Künstliche
Intelligenz) or Formal Foundations (Formale Grundlagen) are helpful.
Learning objectives:
The students can specify data in the Resource Description Format (RDF) and
define schema knowledge with RDF Schema. They are able to derive implicit
consequences based on the RDF and RDFS rules. The students can use the
Web Ontology Language OWL to describe knowledge and to construct simple
ontologies with an ontology editor. They can compute entailed consequences
by employing algorithms for automated reasoning. The students can name te
differences between RDF(S) and OWL and they can evaluate the advantages
and disadvantages of the languages. The students can pose queries using the
SPARQL query language and explain how the answers to a query are computed.
They know the OWL RL profile and can explain how a derivation is made
with the OWL RL rules. Given an application scenario, the students are able
to choose a suitable language and they can predict the consequences of their
design decisions.
Content:
- RDF (Resource Description Framework) and RDF Schema for creating meta
data and simple ontologies
- The Web Ontology Language (OWL) and its extension OWL 2
- Automated reasoning for deriving entailed consequences of an ontology
- The SPARQL query language for RDF
- The OWL RL profile and its rules
- Aspects of ontology engineering
- Practical application in the Semantic Web
36
Literature:
- Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph. Foundations of Semantic
Web Technologies. CRC Press 2009
- Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph, York Sure. Semantic Web
- Grundlagen. Springer 2008
- Steffen Staab, Rudi Studer (Editors). Handbook on Ontologies. Springer 2003
- Tim Berners-Lee. Weaving the Web. Harper 1999 geb./2000 Taschenbuch
- Siegfried Handschuh, Steffen Staab. Annotation for the Semantic Web. 2003
Basis for:
Master’s thesis in the area of intelligent systems and automated reasoning in
the Semantic Web.
Modes of learning
and teaching:
Lecture Foundations of Semantic Web Technologies, 3 SWS (Jun.-Prof. Dr.
Birte Glimm)
Exercise Foundations of Semantic Web Technologies, 1 SWS (Markus Brenner)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
The exam is oral or written depending on the number of participants. The
exact mode is announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade
bonus according to §13 (5) of the study and exam regulations Informatik/Medieninformatik/Software Engineering/Cognitive Systems is given if
the exercise class is passed successfully.
Basierend auf Rev. 1505. Letzte Änderung am 26.05.2015 um 09:52 durch bglimm.
37
3.10 Hands On: Mobile Assessment of Biosignals - Principles and Application
Token / Number:
88csyneu
German title:
Hands On: Physiologische Datenerhebung im Feld - Grundlagen und erste
Schritte
Credits:
4 ECTS
Semester hours:
2
Language:
English
Turn / Duration:
Sporadic (Summer Term) / 1 Semester
Module authority:
Prof. Dr. Martin Baumann
Training staff:
Priv.-Doz. Dr. Cornelia Herbert
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
None
Learning objectives:
The participants shall acquire theoretical as well as practical knowledge about
the recording of biosignals (EEG) in real-time and their mobile and wireless
measurement.
Content:
This course is an introductory course on the mobile assessment of biosignals.
Special focus will be given to wireless recording of EEG data and its applications
(e.g. in sports, neurorehabilitation, human machine interaction). The course
consists of four major teaching units. It starts with a brief introduction into
the basic principles of electroencephalography (EEG). Next, training in wireless
EEG recording will be provided (second block). In the third block, data will be
recorded in a real life situation and analyzed together in the fourth block.
Literature:
Will be announced at the beginning of the course
Basis for:
Modes of learning
and teaching:
Project “Project - Hands on- Mobile Assessment of Biosignals - Principles and
Application” (PD Dr. Cornelia Herbert)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The exam is oral or written depending on the number of participants. The
exact mode is announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the mark in the exam. A grade
bonus according to §13 (5) of the study and exam regulations cognitive systems
is given if the exercises are passed successfully.
Basierend auf Rev. 1587. Letzte Änderung am 08.10.2015 um 12:44 durch mreichert.
38
3.11 Information Processing in Neural Systems
Token / Number:
88csyneu
German title:
Informationsverarbeitung im Nervensystem
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Günther Palm
Training staff:
Prof. Dr. Günther Palm
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Specialization Subject
Computer Science, M.Sc., Core Subject, Theoretische und Mathematische
Methoden der Informatik
Computer Science and Media, M.Sc., Core Subject, Theoretische und Mathematische Methoden der Informatik
Computer Science, M.Sc., Specialization Subject, Neuroinformatik
Computer Science and Media, M.Sc., Specialization Subject, Neuroinformatik
Computer Science and Media, M.Sc., Application Subject, Simulation neuronaler Netze
Requirements
(contentual):
Basic knowledge in mathematics, computer science, and neuroscience
Learning objectives:
The students understand the biological examples of information processing.
They are able to develop models of neurobiological information processing for
similar systems. They are able to develop models based on elementary systems
theory.
Content:
- Information processing in neurobiological systems
- In particular the human visual system: information processing in the retina, the
geniculate, and the visual cortices, visual perception and psychophysics
- Similar topics can be considered for the auditory system
- Synaptic plasticity and adaptivity
- Methods from systems theory
39
Literature:
- P.S. Churchland, T.J. Sejnowski: The Computational Brain. MIT Press, 1999
- P. Dayan, L.F. Abbott: Theoretical Neuroscience. Computational and Mathematical Modeling of Neural Systems. MIT Press, Cambridge, MA, 2005
- M.S. Gazzaniga (ed.): The Cognitive Neuroscience. MIT Press, 1995
- W. Gerstner, W.M. Kistler: Spiking Neuron Models, Single Neurons, Populations, Plasticity, Cambridge University Press, 2002
- E.M. Izhikevich: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. MIT Press, 2005
- E.R. Kandel, J.H.Schwartz, Th.M. Jessel (eds.): Principles of Neural Science,
3.Ed. Elsevier, 1991
- P. Peretto: An Introduction to the Modeling of Neural Networks. Cambridge
University Press, 1992
- R. Unbehauen: Systemtheorie 1. Oldenburg, 2002
- Ch. von Campenhausen: Die Sinne des Menschen, 2. Aufl. Georg Thieme,
1993
- H.R. Wilson: Spikes, Decisions and Actions. Oxford Univ. Press, 1999/2005
Basis for:
None
Modes of learning
and teaching:
Lecture Information Processing in Neural Systems, 3 SWS (Prof. Dr. Günther
Palm)
Exercise Information Processing in Neural Systems, 1 SWS (Prof. Dr. Günther
Palm)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
The exam is oral or written depending on the number of participants. The
exact mode is announced in the lecture.
Requirements
(formal):
none
Grading:
The mark for the module is determined by the exam mark. A grade bonus
according to §13 (5) of the study and exam regulations cognitive systems is
given if the exercise class is passed successfully.
Basierend auf Rev. 1509. Letzte Änderung am 28.05.2015 um 15:20 durch vpollex.
40
3.12 Information Retrieval and Web Mining
Token / Number:
88csyneu
English title:
Information Retrieval and Web Mining
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Martin Theobald
Training staff:
Prof. Dr. Martin Theobald
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Informationssysteme
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Software-Engineering, M.Sc., Specialization Subject, Informationssysteme
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Computer Science and Media, M.Sc., Specialization Subject, Informationssysteme
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Application Subject Data science
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Grundlagenwissen zu Stochastik, wie in der Vorlesung Angewandte Stochastik
I vermittelt, ist von Vorteil, aber nicht zwingend erforderlich. Grundkenntnisse
zu Datenbanken und Informationssystemen sind von Vorteil.
Learning objectives:
Der Kurs bietet den Studierenden folgende Lernziele:
- Die Studierenden erkennen, wie moderne Suchmaschinen funktionieren.
- Die Studierenden analysieren, welche Algorithmen der Ähnlichkeitssuche und
Ranglistengenerierung unterliegen.
- Die Studierenden analysieren, wie diese Algorithmen für die Interessen individueller Benutzer personalisiert werden können.
- Die Studierenden erkennen, wie diese Algorithmen skalierbar auf verteilte Rechnerarchitekturen abgebildet werden könnnen.
- Des Weiteren erkennen die Studierenden, wie große Webdatensammlungen zur
Klassifikation und Ähnlichkeitssuche von Dokumenten effizient analysiert werden können.
41
Content:
-
Information-Retrieval ist eine Disziplin, die zentrale Aspekte der Dokumentenverarbeitung, der automatischen Ranglistengenerierung sowie der skalierbaren
Datenanalyse miteinander verbindet. Ein Kernthema im Information-Retrieval
ist die effektive und effiziente und Bearbeitung von Stichwortanfragen. Dabei
sind moderne Verfahren im Information-Retrieval weder auf reine Stichwortanfragen noch auf Textdokumente beschränkt, sondern können zunehmend flexibel mit den verschiedensten Datenformaten sowie mit natürlichsprachlichen
Benutzeranfragen umgehen.
Der Bereich Web-Mining fokusiert auf eine Art der Informationsverarbeitung,
die unabhängig von spezifischen Benutzeranfragen nach charakteristischen
Mustern in großen Sammlungen von Webdokumenten sucht. Bekannte
Beispiele hierfür sind wohl Google’s PageRank Algorithmus oder Produktempfehlungen bei Amazon. Aktuelle Ansätze im Information-Retrieval und
Web-Mining verfolgen dabei zunehmend Techniken, die aus dem maschinellen
Lernen bzw. der automatischen Sprachverarbeitung stammen, um gezielt strukturierte Informationen aus Textinhalten zu extrahieren und in Form von semantischen Wissensrepräsentationen zu speichern. Wissensbasierte Systeme,
wie beispielsweise Google’s Knowledge Graph, greifen dabei auf reichhaltige
Wissensbasen zurück, die aus Milliarden von Webdokumenten automatisch extrahiert wurden. Zusammenfassend gliedert sich er Inhalt der Vorlesung in
folgende Punkte:
Grundlagen aus der Wahrscheinlichkeitstheorie und statistischen Modellierung.
Boolesche Auswertung von Suchanfragen und Vektorraummodell.
Probabilistische Auswertungsverfahren zur Ranglistengenerierung (ProbabilisticIR, Okapi BM25).
Personalisierte Suche mit Relevanzfeedback (Robertson/Sparck-Jones, Rocchio).
Evaluation von Suchmaschinen (Precision/Recall, MAP, NDCG, etc.).
Indexierung und effiziente Anfrageauswertung (Quit&Continue, verschiedene
Top-k Algorithmen).
Linkanalyse (PageRank, HITS, TrustRank, SpamRank).
Clustering und automatische Klassifikation von Objekten (k-NN, k-Means,
Naive Bayes, SVMs).
Informationsextraktion mit Hilfe maschineller Lernverfahren sowie Grundlegende
Techniken zur Verarbeitung natürlicher Sprache (POS-Tagging, Named-EntityDetection, Dependenzparsing).
Literature:
- Vorlesungsskript
- Introduction to Information Retrieval.
Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze.
Cambridge University Press, 2008.
http://nlp.stanford.edu/IR-book/
- Modern Information Retrieval, 2nd Ed. Ricardo Baeza-Yates, Berthier RibeiroNeto. Addison Wesley, 2011. http://www.mir2ed.org/
- Mining of Massive Datasets. Jure Leskovec, Anand Rajaraman, Jeff Ullman.
Cambridge University Press, 2011. http://www.mmds.org/
Basis for:
Weitere Vorlesungen im Kontext "‘Data Science"’. Masterarbeiten zum Thema
Information Retrieval & Web Mining.
Modes of learning
and teaching:
Lecture Information Retrieval & Web Mining, 3 SWS (
Prof. Dr. Martin Theobald)
Exercise Information Retrieval & Web Mining, 1 SWS ()
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
42
Course assessment
and exams:
Die Modulprüfung erfolgt in Form einer schriftlichen Klausur. Bei geringer
Teilnahme erfolgt die Modulprüfung ggf. mündlich; dies wird zu Beginn der
Veranstaltung bekannt gegeben.
Requirements
(formal):
Keine.
Grading:
Die Modulnote ergibt sich aus der Modulprüfung.
Bei einer erfolgreichen Teilnahme an den Übungen wird dem Studierenden
ein Notenbonus auf die Modulprüfung bis zur nächst besseren Zwischenstufe
gewährt. Eine Notenverbesserung von 5,0 auf 4,0 ist nicht möglich (§13 Absatz
4 der Fachspezifischen Studien- und Prüfungsordnung für die Bachelor- und
Masterstudiengänge Informatik und Medieninformatik). Die genauen Modalitäten werden zu Beginn der Veranstaltung bekannt gegeben.
Basierend auf Rev. 1586. Letzte Änderung am 08.10.2015 um 12:26 durch mreichert.
43
3.13 Intelligente Handlungsplanung
Token / Number:
88csy72012
English title:
Automated Planning
Credits:
6 ECTS
Semester hours:
4
Language:
Deutsch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Susanne Biundo-Stephan
Training staff:
Prof. Dr. Susanne Biundo-Stephan
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Intelligente Systeme
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Computer Science and Media, M.Sc., Specialization Subject, Intelligente Systeme
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Grundkenntnisse in praktischer und theoretischer Informatik; Grundkenntnisse
im Fach Künstliche Intelligenz sind von Vorteil
Learning objectives:
Die Studierenden kennen die Grundprinzipien der intelligenten Handlungsplanung. Dazu gehören die Darstellung von Planungsproblemen in verschiedenen
Formalismen und der Aufbau von Domänenmodellen. Die Studierenden kennen
die wichtigsten Planungsverfahren einschließlich ihrer formalen Eigenschaften
und sind in der Lage entsprechende Planungssysteme zu entwerfen und zu implementieren. Sie sind mit den gängigsten Modellierungsansätzen vertraut, können Modelle für Planungsdomänen entwickeln und deren Adäquatheit beurteilen.
Sie kennen charakteristische Anwendungsfelder und können einschätzen, welche
Planungsverfahren für welche Problemstellungen geeignet sind.
Content:
-
Literature:
- M. Ghallab, D. Nau, P. Traverso: Automated Planning: Theory and Practice,
Morgan Kaufmann, 2004
- Q. Yang: Intelligent Planning - A Decomposition and Abstraction Based Approach, Springer, 1997
- M. Zweben, M.S. Fox: Intelligent Scheduling, Morgan Kaufmann, 1994
Basis for:
Repräsentationsformalismen für Planung
Nichtlineare Planungsverfahren
Planungsgraphen
Planen durch heuristische Suche
Hierarchisches Planen
Hybrides Planen
Deduktives Planen
Intelligentes Scheduling; CSP-basierte Methoden
Planungsanwendungen
–
44
Modes of learning
and teaching:
Lecture Intelligente Handlungsplanung, 2 SWS (Prof. Dr. Susanne BiundoStephan)
Exercise Intelligente Handlungsplanung, 2 SWS (Bastian Seegebarth, M.Sc.)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung erfolgt je nach Teilnehmerzahl mündlich oder schriftlich. Im
Rahmen der Übungen wird der Lernfortschritt überprüft. Studierende, die 50%
der Übungsaufgaben erfolgreich bearbeitet haben, erhalten in der Modulprüfung
einen Notenbonus
Requirements
(formal):
Keine
Grading:
Die Modulnote ergibt sich aus der Modulprüfung. Bei erfolgreicher Teilnahme
an den Übungen wird dem Studierenden ein Notenbonus gemäß §13 (5) der
fachspezifischen Prüfungsordnung Informatik/Medieninformatik/Software Engineering gewährt.
Basierend auf Rev. 1582. Letzte Änderung am 08.10.2015 um 12:01 durch mreichert.
45
3.14 Introduction to Data Science
Token / Number:
88csyneu
English title:
Introduction to Data Science
Credits:
6 ECTS
Semester hours:
4
Language:
English
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Martin Theobald
Training staff:
Prof. Dr. Martin Theobald
Integration of module
into courses of studies:
Computer Science, B.Sc., Main Subject,
Computer Science and Media, B.Sc., Main Subject,
Software-Engineering, B.Sc., Main Subject, Software-Engineering
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Software-Engineering, M.Sc., Core Subject, Software Engineering
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Application Subject Data science
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Keine.
Learning objectives:
Die Studierenden erkennen, wie die verteilte Datenverarbeitung mit aktuellen
Technologien im Bereich der KeyValue-Stores und NoSQL-Datenbanken funktioniert. Die Studenten analysieren, wie eine komplexe Anwendung mittels
dieser Systeme realisiert werden kann und implementieren auch aktiv eine solche
Anwendung im Verlauf des Kurses. Desweiteren vermittelt der Kurs einen
Überblick über die allgemeine Funktionsweise und die theoretischen Grundlagen der verteilten Datenverarbeitung.
46
Content:
Literature:
Der Begriff “Data Science” ist zu einem wichtigen Schlagwort im Umgang mit
großen Datenmengen geworden. DBIS reagiert auf diese aktuelle Entwicklung mit einer neuen Vorlesung Einführung in Data Science, in welcher den
Studierenden bereits im Bachelorstudium die Grundkonzepte der skalierbaren
Verarbeitung von großen Datenmengen in verteilten Rechnerarchitekturen vermittelt werden.
Die Vorlesung gibt Einblicke in die Funktionsweise verteilter Dateisysteme, wie
beispielsweise das verteilte Hadoop-Dateisystem (HDFS), und vermittelt den
Studierenden einen ersten, praxisorientierten Umgang im Programmieren von
verteilten Anwendungen in MapReduce. Des Weiteren ermöglicht der Kurs
einen Einblick in aktuelle Programmierschnittstellen (API’s) und Datenmodelle im sogenannten “Apache-Hadoop Ecosystem”. Dabei sammeln die Studenten ebenfalls praktische Erfahrung mit weiteren Werkzeugen im Bereich der
sogenannten KeyValue-Stores und aktuellen NoSQL-Datenbanken wie Apache
HBase, Apache HIVE, Apache SPARK und MongoDB. Vertiefende Themen
zu den theoretischen Grundlagen der verteilten Datenverarbeitung, zur Modellierung von klassischen Datenbankkonzepten mittels dieser neuen Technologien
und zur Verarbeitung verschiedener Dokumentformate wie beispielsweise Textund XML-Daten, aber auch neuer Datenformate wie JSON runden den Kurs
ab.
Die Vorlesung Einführung in Data Science gibt einen ersten Einblick die oben
genannten Technologien und zeigt diese auch im Zusammenspiel. Der Schwerpunkt liegt in der praxisorientierten Anwendung der zu Grunde liegenden Architekturen, in welcer die Studierenden anhand von wöchentlichen, aufeinander
aufbauenden Programmierübungen ein komplexes Projekt in Hadoop zu implementieren erlernen. Dabei wird auch auf die theoretischen Grundlagen dieser
Technologien eingegangen sowie ein Einblick in die internen Aspekte dieser Systeme gewährt.
- Vorlesungsskript
Basis for:
Der Kurs bietet eine ideale Basis für weitere Projekte und Vertiefungsthemen
im Bereich “Data Science”, welche von DBIS angeboten werden.
Modes of learning
and teaching:
Lecture Einführung in Data Science, 2 SWS (Prof. Martin Theobald)
Laboratory Einführung in Data Science, 2 SWS ()
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung erfolgt in Form einer schriftlichen Klausur. Bei geringer
Teilnahme erfolgt die Modulprüfung ggf. mündlich; dies wird zu Beginn der
Veranstaltung bekannt gegeben.
Als Leistungsnachweise für das Labor sind eine praktische Problemlösung,
schriftliche Kurzberichte sowie eine Ergebnispräsentation zu erbringen.
Requirements
(formal):
Keine.
Grading:
Die Modulnote ergibt sich zu 50% aus der Modulprüfung und zu 50% aus dem
Labor.
Basierend auf Rev. 1585. Letzte Änderung am 08.10.2015 um 12:20 durch mreichert.
47
3.15 Mobile and Ubiquitous Computing
Token / Number:
88csy71599
English title:
Mobile and Ubiquitous Computing
Credits:
6 ECTS
Semester hours:
4
Language:
English
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr.-Ing. Michael Weber
Training staff:
Prof. Dr.-Ing. Michael Weber
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Medieninformatik
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Software-Engineering, M.Sc., Specialization Subject, Verteilte und eingebettete
Systeme
Software-Engineering, M.Sc., Specialization Subject, Mensch-Maschine Interaktion
Computer Science and Media, M.Sc., Core Subject, Mediale Informatik
Computer Science and Media, M.Sc., Specialization Subject, Medieninformatik
Computer Science, Lehramt, Optional Module,
Communications and Computer Engineering, M.Sc., Elective Module, (Inf)
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
none
Learning objectives:
Students know the characteristics, methods, algorithms and technologies of
mobile and ubiquitous systems. They are enabled to apply and transfer this
knowledge to new ubiquitous and context-aware applications and application
scenarios. They have the ability to plan, design and implement ubiquitous
systems and applications.
Content:
-
Literature:
- Schiller: Mobile Communications, 2nd Ed. Addison-Wesley, 2003
- Mischa Schwartz: Mobile Wireless Communications. Cambridge University
Press., 2004
- John Krumm: Ubiquitous Computing Fundamentals. CRC Press, 2009
- Stefan Poslad: Ubiquitous Computing: Smart Devices, Environments and Interactions. Wiley, 2009
Basis for:
Introduction to ubiquitous computing
Mobile devices
Mobile communication
Sensors and context
Ubiquitous user interfaces
System support and middleware for ubiquitous computing
Security and privacy in ubiquitous computing
–
48
Modes of learning
and teaching:
Lecture Mobile and Ubiquitous Computing, 3 SWS (Prof. Dr.-Ing. Michael
Weber)
Exercise Mobile and Ubiquitous Computing, 2 SWS (N.N.)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Oral or written exam depending on number of participants.
Requirements
(formal):
none
Grading:
Exam. Successful participation at the assignments earns a bonus.
Basierend auf Rev. 1542. Letzte Änderung am 16.06.2015 um 12:24 durch mreichert.
49
3.16 Self Regulation: Development, Neuro-Cognition and Psychopathology
Token / Number:
88csyneu
German title:
Selbstregulation: Entwicklung, Neuro-Kognition und Psychopathologie
Credits:
4 ECTS
Semester hours:
2
Language:
German
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Martin Baumann
Training staff:
Prof. Dr. Markus Kiefer
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
- Psychology for non-Psychologists or Computer Science for Psychologists (1st
semester courses)
- Cognitive Systems – Concepts, Terminology, Methods
Learning objectives:
Students acquire basis theoretical concepts, experimental methods and practical
applications of research in human self regulation. They learn to understand and
to discuss scientific articles in a group. Students also learn to present a scientific
topic within a term paper.
Content:
In this seminar, students acquire essential knowledge of theories, methods, topics and empirical findings about the psychology and cognitive neuroscience of
self regulation. They learn to define and to distinguish important functions
of self regulation such as executive control and emotion regulation. Furthermore, they gain insight in the development of self regulation across life span
and in deficits of self regulation in psychiatric disorders. Students learn the interdisciplinary facets of self regulation covering several psychological disciplines
(general psychology, biological psychology, cognitive neuroscience, differential
psychology, clinical psychology and social psychology) and neighboring disciplines such as biology and economics.
Literature:
Goschke, T. (2008). Volition und kognitive Kontrolle. In J. Müsseler (Ed.),
Lehrbuch der Allgemeinen Psychologie (pp. 230-393). Heidelberg: Spektrum,
Akademischer Verlag. Karoly, P. (1993). Mechanisms of Self-Regulation - a
Systems View. Annual Review of Psychology, 44, 23-52. Rothbart, M. K.,
Sheese, B. E., Rueda, M. R., & Posner, M. I. (2011). Developing Mechanisms
of Self-Regulation in Early Life. Emotion Review, 3, 207-213.
Modes of learning
and teaching:
Seminar Self Regulation: Development, Neuro-Cognition and Psychopathology
(Prof. Dr. Markus Kiefer)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
Students have to write a term paper of about 10 pages.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the mark in the exam.
50
Basierend auf Rev. 1587. Letzte Änderung am 08.10.2015 um 12:44 durch mreichert.
51
3.17 Specialization in Cognitive Psychology
Token / Number:
88csyneu
German title:
Grundlagenvertiefung Kognition
Credits:
4 ECTS
Semester hours:
2
Language:
English/German
Turn / Duration:
Every Semester / 1 Semester
Module authority:
Prof. Dr. Anke Huckauf
Training staff:
Dozenten aus dem Institut für Psychologie und Pädagogik
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
None
Learning objectives:
The students learn about selected findings and theoretical approaches in cognitive psychology and their implications for practical as well for theoretical issues.
The students can describe the elementary concepts and methods of contents
and methods in current research on general experimental psychology. They
know about the history of respective approaches and various application fields.
Students can relate respective contemporary research questions to basic theoretical questions as well as to methodological issues.
Content:
- Selected topics in cognitive psychology are presented; the history of a certain
subject; elementary concepts, principles and methods of respective subject; comparing various approaches subject to investigations; Principles of applying respective know-how and methodology
Literature:
- Presented in the respective courses
Basis for:
This module provides the basis for all psychological modules. It also helps for
any of the interdisciplinary topics that are scheduled later in the curriculum.
Modes of learning
and teaching:
Project Regulatory mechanisms in perception, 2 SWS (Prof. Dr. Anke Huckauf)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The exam is oral or written depending on the number of participants. The
exact mode is announced in the lecture.
Requirements
(formal):
Bachelor in Computer Science or related disciplines
Grading:
The mark for the module is determined by the mark in the exam.
Basierend auf Rev. 1545. Letzte Änderung am 16.06.2015 um 14:25 durch vpollex.
52
3.18 Usability Engineering
Token / Number:
88csy72028
English title:
Usability Engineering
Credits:
6 ECTS
Semester hours:
4
Language:
Deutsch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr.-Ing. Michael Weber
Training staff:
Michael Offergeld
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Mensch-Maschine Dialogsysteme
Software-Engineering, M.Sc., Core Subject, Software Engineering
Software-Engineering, M.Sc., Specialization Subject, Mensch-Maschine Interaktion
Computer Science, M.Sc., Specialization Subject, Software-Engineering und
Compilerbau
Computer Science and Media, B.Sc., Main Subject,
Computer Science and Media, M.Sc., Core Subject, Mediale Informatik
Communications and Computer Engineering, M.Sc., Elective Module, (Inf)
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Keine
Learning objectives:
Die Teilnehmer verfügen über Grundkenntnisse der Mensch-MaschineInteraktion (MMI) sowie zugehörige Grundmodelle. Dazu zählen die Kenntnis von Gestaltungsobjekten einer Mensch-Maschine-Schnittstelle, softwareergonomischen Gütekriterien zur Bewertung und Gestaltung von Benutzerfreundlichkeit sowie von Vorgehensweisen zur Entwicklung ergonomischer Systeme. Sie beherrschen die ganzheitliche und durchgängige ergonomische Unterstützung von Entwicklungsprojekten durch Methoden des Usability Engineering
über alle gängigen Phasen einer Systementwicklung hinweg. Sie können diese
Methoden anwenden und praktisch umsetzen und insbesondere Benutzungsschnittstellen nach den Gütekriterien der Benutzerfreundlichkeit bewerten.
53
Content:
Literature:
Die Veranstaltung Usability Engineering gibt eine Einführung in die Grundlagen und Grundmodelle der Mensch-Maschine-Interaktion (MMI). Zunächst werden die zentralen Fragen ”Welche Teilschnittstellen kann man an einer MMI
gestalten/bewerten?”, ”Nach welchen Kriterien kann man eine MMI gestalten/bewerten?” und ”Wie geht man bei der Entwicklung ergonomischer Systeme sinnvollerweise vor?” erörtert und beantwortet. Im Anschluss daran vermittelt die Vorlesung tiefere Einblicke in Konzepte, Methoden und Werkzeuge
einer ganzheitlichen, durchgängigen und ingenieursmäßigen ergonomischen Unterstützung von Systementwicklungsprojekten nach Prinzipen des Usability Engineering. Dabei werden detaillierte Methoden und Ansätze bezogen auf
die Systementwicklungsphasen Projektvorbereitung, Anforderungsanalyse, UserInterface-Entwurf, Integration und Test, Überleitung in die Nutzung sowie
Nutzung und Pflege von interaktiven IT-Systemen vorgesellt. Die vermittelten Grundlagen werden durch zahlreiche Beispiele aus der industriellen Praxis
erläutert und vertieft. Die Inhalte stützen sich auf ein Vorgehen gemäß Usability
Engineering, welches in der wissenschaftlichen Welt etabliert und in der Praxis
erprobt ist.
- Mayhew, D.J.: The Usability Engineering Lifecycle, A Practitioner’s Handbook
for User Interface Design, San Francisco, California, 1999: Morgan Kaufmann
Publishers, Inc.
- Nielsen, Jakob: Usability Engineering, Boston, San Diego, New York, 1993: AP
Professional (Academic Press)
- Oppermann, R.; et. al.: Softwareergonomische Evaluation, Der Leitfaden
EVADIS II, 2. Auflage, deGruyter-Verlag, 1992
- Shneiderman, B.: Designing the User Interface - Strategies for Effective HumanComputer Interaction, Addison-Wesley, 2010
- DATech Deutsche Akkreditierungsstelle Technik in der TGA GmbH: Leitfaden
Usability; http://www.datech.de unter Verfahren & Unterlagen / Prüflaboratorien
Basis for:
–
Modes of learning
and teaching:
Lecture Usability Engineering, 2 SWS (Michael Offergeld)
Exercise Usability Engineering, 2 SWS (Michael Offergeld)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung erfolgt schriftlich.
Requirements
(formal):
Keine
Grading:
Die Modulnote ergibt sich aus der Modulprüfung.
Basierend auf Rev. 1213. Letzte Änderung am 03.02.2014 um 11:05 durch fslomka.
54
3.19 Vision in Man and Machine
Token / Number:
88csy71865
English title:
Vision in Man and Machine
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Sporadic (Summer Term) / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Computer Science, M.Sc., Specialization Subject, Computer Vision
Computer Science and Media, M.Sc., Specialization Subject, Computer Vision
Computer Science and Media, M.Sc., Specialization Subject, Computer Vision
Communications and Computer Engineering, M.Sc., Elective Module,
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
None, prior knowledge acquired from "Computer Vision I" (or similar lecture)
is beneficial
Learning objectives:
The students acquire knowledge about models and mechanisms of visual information processing in biological and technical systems (professional competence).
They become acquainted with formal and algorithmic concepts for the description of processing principles and their coupling with visual information processing in cognitive systems (methodological expertise). They are able to make
use of biological principles and transfer them for computational approaches in
technical applications (transfer and evaluation competence).
Content:
-
Literature:
The following literature list defines a reference. Further hints to specific literature are given at the beginning of the course program:
- E.T. Rolls, G. Deco: Computational Neuroscience of Vision, Oxford Univ. Press,
2002
- C. Curio, H.H. Bülthoff, M.A. Giese (Eds.): Dynamic Faces. MIT Press, 2011
- R. Szeliski: Computer Vision. Springer, 2011
Specific journal and conference papers for detailed discussion of the major topics
will be distributed during the course.
Basis for:
Introduction
Feature extraction and visual cortex
Feature grouping and shape detection
Motion detection and integration
Depth from stereo
Object recognition
Neural processing of faces
Attention
Spatial navigation
Analysis of biological and articulated motion
–
55
Modes of learning
and teaching:
Lecture Vision in Man and Machine, 2 SWS) (Prof. Heiko Neumann)
Exercise Vision in Man and Machine, 2 SWS) (Tobias Brosch)
The exercises split into two parts, each of which contributes 50%. One part
is organized as regular exercises as a means for implementation and practical
testing of the techniques and methods discussed in the lecture. The other part
is organized a journal club where selected papers that cover the topics of the
lecture are discussed in the class. The specific organization will be announced
at the beginning of the course.
Estimation of
effort:
Active Time: (lecture) 45 h
Preparation and Evaluation: 135 h
Sum: 180 h
Course assessment
and exams:
The exams are oral or written depending on the number of participants and
cover the contents of lecture and exercises. The exact modes are announced in
the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade bonus
according to §13 (5) of the study and exam regulations in cognitive systems is
given if the exercise class is passed successfully.
Basierend auf Rev. 1492. Letzte Änderung am 22.04.2015 um 13:21 durch vpollex.
56
4 Applied Subjects
4.1 Cognitive Vision
4.1.1 Project Cognitive Vision - Algorithms and Applications
Token / Number:
88csy?????
English title:
Project Cognitive Vision - Algorithms and Applications
Credits:
16 ECTS
Semester hours:
8
Language:
Englisch
Turn / Duration:
Every Summer Term / 2 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive Vision
Requirements
(contentual):
Basic knowledge in visual information processing (image processing, vision, or
related) are a prerequisite. It is advantageous if participants have successfully
passed "Vision in Man and Machine" or a related course.
Learning objectives:
Students are able to structure and plan a practically oriented topic in the field
of Cognitive Vision. They are familiar with the underlying theory and practical
aspects of computational vision as well as utilities for the analysis and implementation of algorithms in this field. The students can document their results,
evaluate them and finally present those in talks to a greater audience. Students
work in a team of two or more participants. In general, the selected topics focus
on specific research or application problems. Through independent development
of concepts to solve the given problem students gain in-depth insights in the
area of computational vision, from the analysis, to realization and evaluation.
Further, they develop competencies to work in teams, develop goal-oriented
plans, and work jointly at a generic work topic.
57
Content:
The project is organized for two consecutive semesters to develop in-depth
knowledge to realize algorithmic solutions in the field of cognitive computational
vision given an extended problem definition. The problem is outlined at the
beginning of the project in the first semester. Topics can vary but are selected at
the beginning of the semester. During the first phase the range of the contents
that should be covered in the project will be discussed in detail and the relevant
methods will be identified. This is accompanied by a literature search, reading,
and discussion phase. In this phase relevant mathematical and computational
methods will be selected and discussed as well. A preliminary summary will be
generated in written form (short report) and presented orally in a structured
format to a greater audience. Comments and criticisms will be analyzed and
considered to arrive at a revised proposal for the practical project development.
Draft implementations may be developed to supplement this specification phase.
A final report at the end of the first semester fulfills a milestone which builds
the basis for the second semester with the implementation and evaluation phase.
In the second semester, the team starts to define the distribution of work for
the different team members and subgroups. The several parts of the system
will now be implemented. Regular team meetings ensure that the final system’s
integration is kept in the project focus. In a second milestone teams report
about the results achieved in this phase and present this to a greater audience.
In the final phase the teams merge to integrate their components and conduct
evaluations. If the project contains competitive parts in which multiple solutions
to the same problem topic will be developed, their comparative and competitive
evaluation is conducted during this phase. The final project report defines the
third milestone. The results are presented in a final project presentation to a
greater audience.
Literature:
A list of basic material will be distributed at the beginning of the first project
phase.
Basis for:
–
Modes of learning
and teaching:
Project Cognitive Vision - Algorithms and Applications (Prof. Dr. Heiko Neumann)
Estimation of
effort:
Active Time: 80 h
Preparation and Evaluation: 280 h
Sum: 360 h
Course assessment
and exams:
The timely completion of all the milestones (including the final project report),
the oral presentations as well as an active participation in the meetings are
mandatory for successful completion of the project.
Requirements
(formal):
none
Grading:
The mark for the module is determined by the average of the mark given for the
final project report (third milestone) and the final oral project presentation. The
active contribution of each individual to reach the first and second milestones
as well as the short report (in the first semester) are mandatory prerequisites.
Basierend auf Rev. 1589. Letzte Änderung am 08.10.2015 um 12:57 durch mreichert.
58
4.1.2 Project Computational Vision and Image Processing
Token / Number:
88csy71980
English title:
Project Computational Vision and Image Processing
Credits:
8 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Semester / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive Vision
Requirements
(contentual):
Basic knowledge in visual information processing (image processing, vision, or
related) are a prerequisite. It is advantageous if participants have successfully
passed "Vision in Man and Machine" or a related course.
Learning objectives:
Students are able to structure and plan a practically oriented topic in the field
of Cognitive Vision. They are familiar with the underlying theory and practical
aspects of various aspects of computational vision as well as utilities for the
analysis and implementation of algorithms in this field. The students can document their results, evaluate them and finally present those in talks to a greater
audience. Students work in a team of two or more participants. In general,
the selected topics focus on specific research or application problem. Through
independent development of concepts to solve the given problem they acquire
in-depth competencies to work in the area of computational vision, from the
analysis, to realization and evaluation. They develop further competencies to
work in teams, develop goal-oriented plans, and work jointly at a generic work
topic.
Content:
In-depth knowledge is developed to realize algorithmic solutions in the field of
cognitive computational vision and system level solutions for visual information processing for a given problem definition. Different topics will be considered from domains such as, for example, computational vision of single images,
motion sequences (video) or stereo, image processing in biologically inspired
neural architecture, and learning in vision. The project starts with a reading
phase where selected literature is analyzed and relevant algorithms are identified. These algorithms are implemented and evaluated using proper test data.
In cases of methodological comparisons the different outcomes are compared in
a competitive fashion. The results are presented in a final project presentation
to a greater audience and the project together with its results is described in a
project report.
Literature:
A list of basic material will be distributed at the beginning of the first project
phase.
Basis for:
–
Modes of learning
and teaching:
Project Project Computational Vision and Image Processing (Prof. Dr. Heiko
Neumann)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 210 h
Sum: 240 h
59
Course assessment
and exams:
Vortrag zur Aufgabenstellung und Implementierung sowie eine schriftliche
Ausarbeitung The final oral presentations as well as the final project report
are required. The report includes a documentation of the evaluation results
and proper statistical testing.
Requirements
(formal):
Bachelor
Grading:
The mark for the module is determined by the average of the mark given for
the final written project report and the final oral project presentation.
Basierend auf Rev. 1492. Letzte Änderung am 22.04.2015 um 13:21 durch vpollex.
60
4.1.3 Project and Seminar Visual Information Processing
Token / Number:
88csyneu
English title:
Project and Seminar Visual Information Processing
Credits:
8 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Semester / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive Vision
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive Neuroscience
Requirements
(contentual):
Assumes basic knowledge regarding methods and principles in digital image
processing or computational vision.
Learning objectives:
The students are capable to deal with scientific technical literature and apply
scientific methodology for their studies. Students can search and analyze specific literature to present the core contents in a seminar presentation. These
investigations serve as the basis for subsequent design and implementation of
the computational mechanisms as described in the source references. They will
finally test and present the key results of their model implementations.
Content:
A specific topic from the area of cognitive systems is selected and relevant core
literature based on original scientific papers will be discussed. Students will
present the contents and core themes in a seminar talk. Based on this presentation and literature study the content of such literature study will be specified,
implemented and tested. A demonstration together with a final presentation
completes the project part. A written report is finally delivered.
Literature:
The literature that is suited for the seminar and project will be selected in regard
to the the selected topic.
Basis for:
Lectures from the core topic and specialization areas in Cognitive Systems
should be attended to utilize a sound background for application and interdisciplinary work.
Modes of learning
and teaching:
Project Seminar (2 SWS) (Prof. Heiko Neumann)
This module combines seminar and project parts. The introductory part with
literature study and preparation of scientific presentation defines the seminar.
The subsequent specification of the algorithms, implementation and evaluation
defines the project part. The details concerning the contents and the specific
time table are presented to the attendees at the beginning of the semester.
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 210 h
Sum: 240 h
Course assessment
and exams:
The final mark will be determined through the initial seminar presentation, the
final project presentation, and the project report. A minimum achievement for
all three parts is mandatory.
Requirements
(formal):
None
61
Grading:
The mark for the module is determined by the average mark obtained for the
seminar presentation, final project presentation and the project report. Each
one of the three partial contributions is graded. The final mark is determined
by the average of equally weighted grades.
Basierend auf Rev. 1590. Letzte Änderung am 08.10.2015 um 13:07 durch mreichert.
62
4.1.4 Vision
Token / Number:
88csy
English title:
Vision
Credits:
4 ECTS
Semester hours:
2
Language:
Englisch
Turn / Duration:
Sporadic (Summer Term) / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive Vision
Requirements
(contentual):
None, knowledge acquired from successful participation of a lecture in computational vision or related topic is advantageous
Learning objectives:
The students acquire basic knowledge of scientific work and searching for and
dealing with scientific literature (evaluation competencies). They are able to
analyze specific literature and strategies to search for additional literature from
selected sources, extract the key messages, analyze them and evaluate them
(evaluation and presentation competences). The students practice scientific
discourse and discussion of scientific content.
Content:
The seminar discusses a focus theme and basic literature is selected. Such
literature will be presented in the beginning and the students select a particular
topic. After the introductory reading phase in the seminar additional literature
will be selected and studied subsequently (literature search). The students
prepare and present their theme in a ’spotlight’. A summary document will
be prepared and distributed among the other participants. A final extended
seminar presentation is delivered, each of which will be discussed in the greater
audience. The written summary papers serve as basis to prepare the discussion.
Literature:
A set of papers will be selected with reference to the specific thematic focus of
the seminar.
Basis for:
–
Modes of learning
and teaching:
Seminar Vision (
Prof. Dr. Heiko Neumann)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The final mark will be determined through the initial ’spotlight’ presentation,
the final seminar presentation, and the seminar report. A minimum achievement
for all three parts is mandatory.
Requirements
(formal):
None
63
Grading:
The mark for the module is determined by the average mark obtained for the
’spotlight’ presentation, the final seminar presentation and the seminar report.
The final mark is determined by the average of equally weighted grades given
for each part contributions.
Basierend auf Rev. 1498. Letzte Änderung am 24.04.2015 um 09:37 durch hneumann.
64
4.1.5 Vision in Man and Machine (in Applied Subject)
Token / Number:
88csy71977
English title:
Vision in Man and Machine (in Applied Subject)
Credits:
4 ECTS
Semester hours:
3
Language:
Englisch
Turn / Duration:
Sporadic (Summer Term) / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Computer Science and Media, M.Sc., Application Subject, Computer Vision
Cognitive Systems, M.Sc., Application Subject Cognitive Vision
Requirements
(contentual):
None, prior knowledge acquired from "Computer Vision I" (or similar lecture)
is beneficial
Learning objectives:
The students acquire knowledge about models and mechanisms of visual information processing in biological and technical systems (professional competence).
They become acquainted with formal and algorithmic concepts for the description of processing principles and their coupling with visual information processing in cognitive systems (methodological expertise). They are able to make
use of biological principles and transfer them for computational approaches in
technical applications (transfer and evaluation competence).
Content:
-
Introduction
Feature extraction and visual cortex
Feature grouping and shape detection
Motion detection and integration
Depth from stereo
Object recognition
Neural processing of faces
Attention
Spatial navigation
Analysis of biological and articulated motion
Literature:
The following literature list defines a reference. Further hints to specific literature are given at the beginning of the course program:
- E.T. Rolls, G. Deco: Computational Neuroscience of Vision, Oxford Univ. Press,
2002
- C. Curio, H.H. Bülthoff, M.A. Giese (Eds.): Dynamic Faces. MIT Press, 2011
- R. Szeliski: Computer Vision. Springer, 2011
Basis for:
–
Modes of learning
and teaching:
Lecture Vision in Man and Machine, 2 SWS) (Prof. Heiko Neumann)
Exercise Vision in Man and Machine, 1 SWS) (Tobias Brosch)
Exercises serve as a means for implementation and practical testing of the
techniques and methods discussed in the lecture.
Estimation of
effort:
Active Time: (lecture) 45 h
Preparation and Evaluation: 75 h
Sum: 120 h
65
Course assessment
and exams:
The exams are oral or written depending on the number of participants and
cover the contents of lecture and exercises. The exact modes are announced in
the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade bonus
according to §13 (5) of the study and exam regulations in cognitive systems is
given if the exercise class is passed successfully.
Basierend auf Rev. 1514. Letzte Änderung am 07.06.2015 um 18:06 durch hneumann.
66
4.2 Visual Computing
4.2.1 Data Visualization
Token / Number:
88csy??????
English title:
Data Visualization
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Timo Ropinski
Training staff:
Prof. Dr. Timo Ropinski
Integration of module
into courses of studies:
Computer Science, B.Sc., Main Subject,
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, B.Sc., Main Subject,
Computer Science and Media, M.Sc., Core Subject, Mediale Informatik
Software-Engineering, B.Sc., Main Subject,
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Cognitive Systems, M.Sc., Specialization Subject
Cognitive Systems, M.Sc., Application Subject Visual Computing
Computer Science, Lehramt, Optional Module,
Requirements
(contentual):
Grundlegende Kenntnisse in der Computergrafik werden vorausgesetzt.
Learning objectives:
Die Studierenden kennen grundlegende Konzepte und Algorithmen aus dem
Bereich Visualisierung und können diese anwenden. Sie sind in der Lage, abstrakte und räumliche Daten so zu visualisieren, dass gewünschte Zusammenhänge klar verständlich werden. Des Weiteren können sie ein breites Spektrum
an Visualisierungstechniken technisch umsetzen, oder sofern in Anwendungen
verfügbar, erfolgreich anwenden.
Content:
Es werden die Grundlagen aus verschiedenen Bereichen der Visualisierung
vermittelt. Dabei werden die bearbeiteten Techniken in den Kontext der
Visualisierungs-Pipeline eingeordnet, welche als roter Faden für das Modul gilt.
Der Hauptfokus liegt dabei auf interaktiven Visualisierungstechniken, welche es
dem Benutzer erlauben mit den Visualisierungen zu interagieren, um beispielsweise die darzustellenden Daten zu filtern oder Darstellungsparameter zu verändern. Es werden die folgenden Themen behandelt:
Einordnung der Teilgebiete
Die Visualisierungs Pipeline
Datenstrukturen für räumliche Daten
Visualisierung von Skalar-, Vektor und Tensor-Feldern
Visualisierung Multi-Parametrischer Daten
Glyph-basierte Techniken
Ausgewählte Aspekte der visuellen Wahrnehmung
Anwendung moderner Visualisierungs Systeme
ILIAS:
–
67
Literature:
-
Es existiert kein Lehrbuch, welches alle behandelten Aspekte abdeckt. Daher
wird spezielle Literatur zu den einzelnen Kapiteln in der Vorlesung angegeben.
Als übergreifende Werke sind die folgenden Bücher zu nennen:
Matt Ward, Georges Grinstein, Daniel Keim: Interactive Data Visualization –
Foundations, Techniques, and Applications, CRC Press 2010.
Tamara Munzner: Visualization Analysis and Design, AK Peters 2014.
Colin Ware: Information Visualization: Perception for Design, Morgan Kaufmann 2012.
Alexandru C. Telea: Data Visualization: Principles and Practice, AK Peters
2014.
Basis for:
–
Modes of learning
and teaching:
Lecture Visualisierung, 3 SWS (Prof. Dr. Timo Ropinski)
Exercise Visualisierung, 1 SWS (Robin Skånberg)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung, welche die Inhalte aus der Vorlesung und den Übungen
abdeckt, erfolgt schriftlich oder mündlich.
Requirements
(formal):
Grading:
Die Modulnote ergibt sich aus der Modulprüfung. Bei einer erfolgreichen
Teilnahme an den Übungen wird dem Studierenden ein Notenbonus auf die
Modulprüfung bis zur nächst besseren Zwischenstufe gewährt. Eine Notenverbesserung von 5,0 auf 4,0 ist nicht möglich (§13 Absatz 4 der Fachspezifischen Studien- und Prüfungsordnung für die Bachelor- und Masterstudiengänge
Informatik und Medieninformatik). Die genauen Modalitäten werden zu Beginn
der Veranstaltung bekannt gegeben.
Basierend auf Rev. 1544. Letzte Änderung am 16.06.2015 um 14:01 durch vpollex.
68
4.2.2 Visual Computing
Token / Number:
88csy??????
English title:
Visual Computing
Credits:
4 ECTS
Semester hours:
2
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Timo Ropinski
Training staff:
Prof. Dr. Timo Ropinski
Integration of module
into courses of studies:
Computer Science, M.Sc., Seminar,
Software-Engineering, M.Sc., Seminar,
Computer Science and Media, M.Sc., Seminar,
Cognitive Systems, M.Sc., Application Subject Visual Computing
Requirements
(contentual):
Grundlegende Kenntnisse in den Bereichen Computergrafik und Visualisierung
können hilfreich sein, sind aber keine notwendige Voraussetzung.
Learning objectives:
Die Studierenden erlernen es sich wissenschaftliche Themen selbstständig zu erarbeiten. Dabei wird sich Themen-spezifisches Wissen individuell unter Hinzunahme der gestellten Primärliteratur und der hinzugezogenen Sekundärquellen
angeeignet. Dieses Wissen wird dann im Rahmen einer schriftlichen Abhandlung wissenschaftlich aufbereitet und während des Seminars in Form eines
mündlichen Vortrags präsentiert. Die vertiefte Auseinandersetzung mit dem
zu bearbeitenden Thema stellt eine ideale Voraussetzung für die Anfertigung
einer Abschlussarbeit im Bereich Visual Computing dar.
Content:
Zu Anfang des Seminars werden den Studierenden mögliche Themen vorgestellt.
Nach der Themenwahl bearbeitet jeder Studierende das gewählte Thema unter
individueller Betreuung.
ILIAS:
–
Literature:
Originalarbeiten aus wissenschaftlichen Zeitschriften und Konferenzen.
Basis for:
–
Modes of learning
and teaching:
SeminarVisual Computing, 2 SWSProf. Dr. Timo Ropinski
Estimation of
effort:
Active Time: 40 h
Preparation and Evaluation: 80 h
Sum: 120 h
Course assessment
and exams:
Leistungsnachweis über erfolgreiche Teilnahme. Diese umfasst Ausarbeitung,
Vortrag und Mitarbeit, sowie regelmäßige Anwesenheit im Seminar.
Requirements
(formal):
Keine
Grading:
Das Modul ist unbenotet.
69
Basierend auf Rev. 1639. Letzte Änderung am 12.10.2015 um 07:36 durch vpollex.
70
4.3 Pattern Recognition
4.3.1 Algorithms for Emotion Recognition in Human Computer Interaction
Token / Number:
88csy8827972534
English title:
Algorithms for Emotion Recognition in Human Computer Interaction
Credits:
16 ECTS
Semester hours:
8
Language:
Englisch
Turn / Duration:
Every Winter Term / 2 Semester
Module authority:
Prof. Dr. Günther Palm
Training staff:
Dr. Friedhelm Schwenker
Prof. Dr. Harald Traue
Dr. Steffen Walter
Dr. Dilana Hazer
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Pattern recognition
Requirements
(contentual):
none
Learning objectives:
Students are able to organize a practical task in the field of affective computing.
They are familiar with the underlying theory and the practical issues in the field
of emotion recognition. Students are able to analyze multimodal emotional
data sets and know the relevant feature extraction and classification techniques.
Students can document (written form) and orally present their achieved results.
Students learn to work in small teams of two or three persons to work according
to defined project plan.
Content:
The project is organized for two consecutive semesters to develop and to implement solutions in the field of multimodal emotion recognition. The concrete
problem is defined in the beginning of the project in the first semester. The
first phase of the project is devoted to literature search and discussion of the
relevant topics in the field of automatic recognition of emotions. The results of
this first phase are presented as preliminary report and oral presentation. In the
second phase several basic algorithms are implemented as standalone solutions
and evaluated in detail. The result of this phase is a concrete project plan
(in written form), including work packages and milestones. The project plan
is presented to and discussed with a greater audience at the end of the first
semester. This detailed plan is used for carrying out the project in the second
semester. In the second semester the overall system is then fully implemented
according to this plan. Final result of this project, is an automatic emotion
recognition system, project report and oral presentation of the system itself and
the achieved results.
Literature:
A list of basic material will be distributed at the beginning of the first project
phase.
Basis for:
–
Modes of learning
and teaching:
Project Algorithms for emotion recognition in human computer interaction (Dr.
Friedhelm Schwenker, Prof. Dr. Harald Traue, Dr. Steffen Walter, Dr. Dilana
Hazer)
71
Estimation of
effort:
Active Time: 120 h
Preparation and Evaluation: 360 h
Sum: 480 h
Course assessment
and exams:
The timely completion of all the milestones (including the final project report),
the oral presentations as well as an active participation in the meetings are
mandatory for successful completion of the project.
Requirements
(formal):
none
Grading:
The mark for the module is determined by the average of the mark given for
the final project report and the final oral project presentation. The active
contribution of each individual to reach the first and second milestones as
well as the reports and oral presentations in the first semester are mandatory
prerequisites.
Basierend auf Rev. 1626. Letzte Änderung am 09.10.2015 um 17:24 durch mreichert.
72
4.3.2 Neural Networks (in Applied Subject)
Token / Number:
88csy8827972531
English title:
Neural Networks (in Applied Subject)
Credits:
4 ECTS
Semester hours:
3
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Günther Palm
Training staff:
Dr. Friedhelm Schwenker
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Pattern recognition
Requirements
(contentual):
Basic knowledge in computer science and mathematics-
Learning objectives:
The Students acquire theoretical knowledge in artificial neural networks and are
able to apply this to practical applications of small or medium size.
Content:
Literature:
Topics of the lecture Neural Networks (in applied subject) are
Biological foundations of neural networks
Models of neural networks in technical applications
General local learning rules
Feedforward neural networks (single and multi-layer, radial basis function nets)
Recurrent neural networks (Hopfield networks, echo state networks)
Supervized, unsupervised and semi-supervised learning in neural nets
- Bishop, Chris: Neural Networks for Pattern Recognition, Oxford University
Press, 1995
Basis for:
–
Modes of learning
and teaching:
Lecture Neural Networks (in applied subject) (Dr. Friedhelm Schwenker)
Exercise Neural Networks (in applied subject) (Dr. Friedhelm Schwenker)
Estimation of
effort:
Active Time: 45 h
Preparation and Evaluation: 75 h
Sum: 120 h
Course assessment
and exams:
Written exam.
Requirements
(formal):
none
Grading:
The mark of the module is determined by the mark of the exam.
Basierend auf Rev. 1639. Letzte Änderung am 12.10.2015 um 07:36 durch vpollex.
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4.3.3 Pattern Recognition
Token / Number:
88csy8827972532
English title:
Pattern Recognition
Credits:
4 ECTS
Semester hours:
2
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Günther Palm
Training staff:
Prof. Dr. Günther Palm
Dr. Friedhelm Schwenker
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Pattern recognition
Requirements
(contentual):
none
Learning objectives:
The Students learn to deal with a scientific topic in the field of pattern recognition, to grasp the relevant information and key ideas from the literature, and
they learn how to present the material in a scientific talk (including discussion)
and in a written report.
Content:
-
Topics of the Seminar Pattern Recognition are for instance
Features in speech, images or video data
Feature extraction, reduction and selection
Learning algorithms (e.g. supervized, partially supervized)
Basic classifier architectures (e.g., statistical, syntactical, neural)
Clustering algorithms
Evaluation classifiers and cluster results
Literature:
In the first class a list of basic material will be distributed and topics assigned.
Basis for:
–
Modes of learning
and teaching:
Seminar Seminar Pattern Recognition (Prof. Dr. Günther Palm, Dr. Friedhelm
Schwenker)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
Continued participation in classes; preparation of term paper; oral presentation.
Requirements
(formal):
none
Grading:
The modul is ungraded.
Basierend auf Rev. 1639. Letzte Änderung am 12.10.2015 um 07:36 durch vpollex.
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4.3.4 Pattern Recognition and Machine Learning Algorithms
Token / Number:
88csy8827972535
English title:
Pattern Recognition and Machine Learning Algorithms
Credits:
16 ECTS
Semester hours:
8
Language:
Englisch
Turn / Duration:
Every Summer Term / 2 Semester
Module authority:
Prof. Dr. Günther Palm
Training staff:
Dr. Friedhelm Schwenker
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Pattern recognition
Requirements
(contentual):
none
Learning objectives:
Students are able to organize a practical task in the field of pattern recognition
using machine learning techniques. They are familiar with the underlying theory
and the practical issues in the concrete area, e.g. speech processing. Students
are able to analyze the relevant, typical huge data sets and know the relevant
feature extraction and classification techniques. Students can document (written form) and orally present their achieved results. Students learn to work in
small teams of two or three persons to work according to defined project plan.
Content:
The project is organized for two consecutive semesters to develop and to implement solutions in the field of pattern recognition. The concrete problem
is defined in the beginning of the project in the first semester, possible topics
are speech recognition, speaker identification or verification, The first phase of
the project is devoted to literature search and discussion of the relevant topics
in the area of interest, e.g. speech processing. The results of this first phase
are presented as preliminary report and oral presentation. In the second phase
several basic algorithms are implemented as standalone solutions and evaluated
in detail. The result of this phase is a concrete project plan (in written form),
including work packages and milestones. The project plan is presented to and
discussed with a greater audience at the end of the first semester. This detailed
plan is used for carrying out the project in the second semester. In the second
semester the overall system is then fully implemented according to this plan.
Final result of this project, is an automatic emotion recognition system, project
report and oral presentation of the system itself and the achieved results.
Literature:
A list of basic material will be distributed at the beginning of the first project
phase.
Basis for:
–
Modes of learning
and teaching:
Project Algorithms for emotion recognition in human computer interaction (Dr.
Friedhelm Schwenker)
Estimation of
effort:
Active Time: 120 h
Preparation and Evaluation: 360 h
Sum: 480 h
75
Course assessment
and exams:
The timely completion of all the milestones (including the final project report),
the oral presentations as well as an active participation in the meetings are
mandatory for successful completion of the project.
Requirements
(formal):
none
Grading:
The mark for the module is determined by the average of the mark given for
the final project report and the final oral project presentation. The active
contribution of each individual to reach the first and second milestones as
well as the reports and oral presentations in the first semester are mandatory
prerequisites.
Basierend auf Rev. 1639. Letzte Änderung am 12.10.2015 um 07:36 durch vpollex.
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4.3.5 Reinforcement Learning
Token / Number:
88csy8827972533
English title:
Reinforcement Learning
Credits:
8 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Günther Palm
Training staff:
Dr. Friedhelm Schwenker
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Pattern recognition
Requirements
(contentual):
none
Learning objectives:
Students are able to organize a practical task in the field of reinforcement learning (RL). They are familiar with the underlying theory and the practical issues
of RL methods. In concrete scenarios students are able to define and design
RL solutions and know the relevant RL techniques. Students can document
(written form) and orally present their achieved results. Students learn to work
in small teams of two or three persons to work according to defined project
plan.
Content:
The project is organized for one semesters to develop and to implement a
concrete RL solution. The concrete problem is defined in the beginning of the
semester, e.g. a game playing agent The first phase of the project is devoted
to literature search and discussion of the relevant topics in RL. The results of
this first phase are presented as preliminary report and oral presentation. In the
second phase several basic algorithms are implemented as standalone solutions
and evaluated in detail. The result of this phase is a concrete project plan (in
written form), including work packages and milestones. The project is presented
to and discussed by the end of the first semester.
Literature:
A list of basic material will be distributed in the first class.
Basis for:
–
Modes of learning
and teaching:
Project Reinforcement learning (Dr. Friedhelm Schwenker)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 180 h
Sum: 240 h
Course assessment
and exams:
The timely completion of all the milestones (including the final project report),
the oral presentations as well as an active participation in the meetings are
mandatory for successful completion of the project.
Requirements
(formal):
none
77
Grading:
The mark for the module is determined by the average of the mark given for
the final project report and the final oral project presentation. The active
contribution of each individual to reach the milestones as well as the reports
and oral presentations are mandatory prerequisites.
Basierend auf Rev. 1639. Letzte Änderung am 12.10.2015 um 07:36 durch vpollex.
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4.4 Cognitive Ergonomics
4.4.1 Instructional Design and Technology
Token / Number:
88csyneu
German title:
Instructional Design and Technology
Credits:
4 ECTS
Semester hours:
2
Language:
English and German
Turn / Duration:
Sporadic (Winter Term2015) / 1 Semester
Module authority:
Prof. Dr. Tina Seufert
Training staff:
Prof. Dr. Tina Seufert
Dipl. Paed. Felix Wagner
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive ergonomics
Requirements
(contentual):
None.
Learning objectives:
The Students acquire deep knowledge about theoretical concepts and practical
applications of different aspects in Instructional Design and Technologie for supporting teaching and learning processes. They are able to apply this knowledge
to practically relevant problems.
Content:
During the course students will learn and apply different concepts, definitions
and methods to support teaching and learning processes by instructional design
principles and technological tools. They will learn how to effectively plan, implement and evaluate educational technologies and applications (i.e. learning
management systems, social media, mobile learning) and media (text, picture,
video, audio) to support learning, communication and collaboration in different
contexts. Additionally, students will learn various theoretical perspectives and
research findings in this field.
Literature:
Will be announced at the beginning of the course
Basis for:
Modes of learning
and teaching:
Seminar Instructional Design and Technology (Dipl.-Paed. Felix Wagner, Prof.
Dr. Tina Seufert)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The students will have to continually participate, present a topic of the field
orally and provide the presentation in written form. The exact form will be
announced in the course. Additionally some exercises might be assigend.
Requirements
(formal):
None
Grading:
The mark for the module is determined by an unweighted average of the marks
for presentation, the written documents of the presentation and if assigned of
the exercises.
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Basierend auf Rev. 1550. Letzte Änderung am 17.06.2015 um 08:23 durch vpollex.
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4.4.2 Project - Driver-Vehicle Interaction
Token / Number:
88csyneu
German title:
Driver-Vehicle Interaction
Credits:
4 ECTS
Semester hours:
2
Language:
English
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Martin Baumann
Training staff:
Prof. Dr. Martin Baumann
Members of the Institute of Psychology and Education, Dep. Human Factors
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject
Cognitive Systems, M.Sc., Application Subject Cognitive ergonomics
Requirements
(contentual):
None
Learning objectives:
The participants shall acquire knowledge about the essential and relevant theoretical concepts, methods and challenges related to the development of a
man-machine interface for a highly automated vehicle. This knowledge will be
practiced and deepened by the practical development of a prototype.
Content:
The goal of the course is the development of a display concept for a highly
automated vehicle. This display shall enable the driver to recognize fast and
reliably the vehicle’s state and planned behaviour and thereby facilitate the
driver’s generation of an adequate situation model. At the beginning of the
course basic models and empirical findings relevant for the design of drivervehicle interaction will be discussed. Additionally, methodological approaches
in human-centred design will be presented and practiced. After the treatment of
these basic concepts and approaches a prototype concept for the display of the
research vehicle of the Institute for Measurement, Control and Microtechnology
will be developed together with the members of the Institute. This prototype
will be presented at the end of the course.
Literature:
Will be announced at the beginning of the course
Basis for:
Modes of learning
and teaching:
Project Driver-Vehicle-Interaction (Members of the Institute of Psychology and
Education, Dep. Human Factors)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The students will have to continually participate, present a topic of the field
orally, provide the presentation in written form and write a short research report
about their project. The exact form will be announced in the course. Additionally some exercises might be assigend.
Requirements
(formal):
None
81
Grading:
The mark for the module is determined by an unweighted average of the marks
all written documents (presentation, research report and assigned exercises.
Basierend auf Rev. 1550. Letzte Änderung am 17.06.2015 um 08:23 durch vpollex.
82
4.4.3 Project Cognitive Ergonomics
Token / Number:
88csyneu
German title:
Projekt kognitive Ergonomie
Credits:
6 ECTS
Semester hours:
4
Language:
English
Turn / Duration:
Every Semester / 2 Semester
Module authority:
Prof. Dr. Anke Huckauf
Training staff:
Prof. Dr. Anke Huckauf
Mitarbeiter des Lehrstuhls Allgemeine Psychologie
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive ergonomics
Requirements
(contentual):
- Psychology for non-Psychologists or Computer Science for Psychologists (1st
semester courses)
- Cognitive Systems - Concepts, Terminology, Methods
Learning objectives:
The students possess a deep knowledge about selected topics in cognitive ergonomics, their theoretical foundations, methodological approaches and empirical findings. The can transfer this knowledge to new questions and can implement respective methodologies for research. They know about the possible
potentials and limits of certain methodological approaches.
Content:
After some key readings, students will focus on selected topics in the field of
cognitive ergnomics. They will develop an own research question, select and
implement neccessary methodological requirements, conduct an experimental
study, analyze its outcomes and provide their insights in scientific form.
Modes of learning
and teaching:
Project Cognitive Ergonomics (Prof. Dr. Anke Huckauf)
Estimation of
effort:
Active Time: 90 h
Preparation and Evaluation: 90 h
Sum: 180 h
Course assessment
and exams:
The exam consists of a presentation of the own work at the beginning, in the
middle, and at the end of the project, and of a written manuscript.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the mark for the written report (70%)
as well as by the slides used for the three oral presentations (10% each).
Basierend auf Rev. 1550. Letzte Änderung am 17.06.2015 um 08:23 durch vpollex.
83
4.4.4 Psychology of Automation
Token / Number:
88csyneu
German title:
Psychology of Automation
Credits:
4 ECTS
Semester hours:
2
Language:
English and German
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Martin Baumann
Training staff:
Prof. Dr. Martin Baumann
Dr.-Ing. Nicola Fricke
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive ergonomics
Cognitive Systems, M.Sc., Interdisciplinary Subject Human-computer dialogue
Requirements
(contentual):
None.
Learning objectives:
The students possess a deep knowledge about important theoretical concepts,
methodological approaches and empirical findings in the area of human-machine
automation. They are able to apply this knowledge to successfully analyze and
solve practically relevant problems.
Content:
During the course the students will learn various definitions and taxonomies
of automation. The will become acquainted with basic concepts, applications,
impacts and drawbacks of automation in human-machine systems. This includes ironies of automation, degrees of automation and specific approaches,
e.g. adaptive automation, human-centered automation, human-machine cooperation. The students will learn about various effects of automation and
about related concepts such as situation awareness, trust and workload, research findings in the application fields medical domain, aviation and surface
transportation.
Literature:
Will be announced at the beginning of the course
Basis for:
Cognitive Systems, M.Sc., Application Subject: Cognitive Ergonomics; Cognitive Systems, M.Sc., Interdisciplinary Subject: Human-Computer Dialogue
Modes of learning
and teaching:
Seminar Psychology of Automation (Dr.-Ing. Nicola Fricke, Prof. Dr. Martin
Baumann)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The students will have to continually participate, present a topic of the field
orally and provide the presentation in written form. The exact form will be
announced in the course. Additionally some exercises might be assigend.
Requirements
(formal):
None
Grading:
The mark for the module is determined by an unweighted average of the marks
for presentation, the written documents of the presentation and if assigned of
the exercises.
84
Basierend auf Rev. 1551. Letzte Änderung am 17.06.2015 um 09:07 durch vpollex.
85
4.4.5 Transportation Human Factors
Token / Number:
88csyneu
German title:
Transportation Human Factors
Credits:
4 ECTS
Semester hours:
2
Language:
English and German
Turn / Duration:
Sporadic (Summer Term 2016) / 1 Semester
Module authority:
Prof. Dr. Martin Baumann
Training staff:
Prof. Dr. Martin Baumann
Members of the Insitute of Psychology and Education, Dept. Human Factors
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive ergonomics
Cognitive Systems, M.Sc., Interdisciplinary Subject Human-computer dialogue
Requirements
(contentual):
None.
Learning objectives:
The students possess a deep knowledge about important theoretical concepts,
methodological approaches and empirical findings in the area of transportation
human factors. The are able to apply this knowledge to successfully analyze
and solve practically relevant problems.
Content:
During the course the students will become acquainted with essential issues,
methods and theoretical concepts of the domain of transportation human factors. These include methodological approaches in the analysis of driver behaviour, psychological foundations of driver behaviour, the effect of driver assistance systems on driver behaviour and current issues in the design of drivervehicle interaction.
Literature:
Will be announced at the beginning of the course
Basis for:
Cognitive Systems, M.Sc., Application Subject: Cognitive Ergonomics; Cognitive Systems, M.Sc., Interdisciplinary Subject: Human-Computer Dialogue
Modes of learning
and teaching:
Seminar Transportation Human Factors (Prof. Dr. Martin Baumann or Members
of the Institute of Psychology and Education, Dept. Human Factors)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The students will have to continually participate, present a topic of the field
orally and provide the presentation in written form. The exact form will be
announced in the course. Additionally some exercises might be assigend.
Requirements
(formal):
None
Grading:
The mark for the module is determined by an unweighted average of the marks
for presentation, the written documents of the presentation and if assigned of
the exercises.
Basierend auf Rev. 1551. Letzte Änderung am 17.06.2015 um 09:07 durch vpollex.
86
4.5 Data Science
4.5.1 Big Data Analytics
Token / Number:
88csyneu
English title:
Big Data Analytics
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Martin Theobald
Training staff:
Prof. Dr. Martin Theobald
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Informationssysteme
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Software-Engineering, M.Sc., Specialization Subject, Informationssysteme
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Computer Science and Media, M.Sc., Specialization Subject, Informationssysteme
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Application Subject Data science
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Grundkenntnisse zu “Datenbanken und Informationssytemen” und “Data Science”, wie sie beispielsweise in der Vorlesung Einführung in Data Science vermittelt werden, sind von Vorteil.
Learning objectives:
Der Kurs vermittelt den Studierenden einen detaillierten Einblick in die Funktionsweise und die theoretischen Grundlagen zur skalierbaren Analyse und verteilten
Verarbeitung von großen Datenmengen. Die Studierenden erkennen, welche
Datenstrukturen und Algorithmen der verteilten Analyse von großen Datenmengen zu Grunde liegen. Des Weiteren sind die Studierenden in der Lage, komplexe
Anwendungen mittels dieser Ansätze zu realisieren.
87
Content:
-
-
Literature:
Die Mastervorlesung Big Data Analytics vertieft die Grundkenntnisse, welche
die Studierenden im Bachelorstudiengang in den Bereichen “Datenbanken und
Informationssysteme” sowie “Data Science” erlangt haben. Die Vorlesung geht
eingehend auf die Funktionsweisen und die theoretischen Grundlagen verteilter
Informationssysteme sowie der verteilten Datenanalyse ein. Die Vorlesung beginnt mit den statistischen Grundlagen zur verteilten Datenverarbeitung und
legt insbesondere einen Fokus auf die Ausführung von verteilten Datenbankoperationen im gesamten Spektrum von “Create, Read, Update, Delete” (CRUD)
mittels klassischer SQL und aktueller NoSQL-Architekturen.
Synchronisationsverfahren, Recovery sowie Client-Server und Client-Client Architekturen von verteilten Dateisystemen in Apache Hadoop und MapReduce.
Hauptspeicherorientierte, verteilte Datenverarbeitung in Apache Spark.
Exakte, parallele Ausführung von traditionellen Transaktionsmodellen wie
“Atomicity, Consistency, Isolation und Durabiltiy” (ACID) sowie relaxierte Varianten mit eventueller Konsistenz (“CAP Theorem”)
Verteilungs- und Partitionierungsstrategien für große Datenmengen (“Sharding”)
mit MapReduce
Skalierbare Ausführung von analytischen Anfragen im Bereich OLAP und OLTP
Verteilte Graphdatenbanksysteme
Weitere Anwendungen im Bereich des Maschinellen Lernens und der Visualisierung von großen Datenmengen
Zusammenfassend bietet die Vorlesung Big Data Analytics einen detaillierten
Einblick in die oben genannten Technologien und zeigt diese in ihrem Zusammenspiel. Im Gegensatz zum Bachelorkurs “Einführung in Data Science” liegt
hier der Schwerpunkt in den theoretischen Grundlagen und statistischen Methoden, die der Datenverarbeitung in verteilten Informationssystemen zu Grunde
liegen.
- Vorlesungsskript
Basis for:
Der Kurs bietet eine ideale Basis für weitere Projekt- und Masterarbeiten in
den Bereichen “Datenbanken und Informationssysteme” sowie “Data Science”,
welche von DBIS angeboten werden.
Modes of learning
and teaching:
Lecture Big Data Analytics, 3 SWS (Prof. Martin Theobald)
Exercise Big Data Analytics, 1 SWS ()
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung erfolgt in Form einer schriftlichen Klausur. Bei geringer
Teilnahme erfolgt die Modulprüfung ggf. mündlich; dies wird zu Beginn der
Veranstaltung bekannt gegeben.
Requirements
(formal):
Keine.
Grading:
Die Modulnote ergibt aus der Modulprüfung.
Bei einer erfolgreichen Teilnahme an den Übungen wird dem Studierenden
ein Notenbonus auf die Modulprüfung bis zur nächst besseren Zwischenstufe
gewährt. Eine Notenverbesserung von 5,0 auf 4,0 ist nicht möglich (§13 Absatz
4 der Fachspezifischen Studien- und Prüfungsordnung für die Bachelor- und
Masterstudiengänge Informatik und Medieninformatik). Die genauen Modalitäten werden zu Beginn der Veranstaltung bekannt gegeben.
Basierend auf Rev. 1624. Letzte Änderung am 09.10.2015 um 17:05 durch mreichert.
88
4.5.2 Business Process Intelligence
Token / Number:
88csy71997
English title:
Business Process Intelligence
Credits:
6 ECTS
Semester hours:
4
Language:
Deutsch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Manfred Reichert
Training staff:
Prof. Dr. Peter Dadam
Prof. Dr. Manfred Reichert
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Informationssysteme
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Computer Science and Media, M.Sc., Specialization Subject, Informationssysteme
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Application Subject Data science
Requirements
(contentual):
Grundlagenwissen zu Datenbanken und Informationssystemen, wie es in den
Modulen Datenbanksysteme – Konzepte und Modelle und Business Process
Management vermittelt wird.
Learning objectives:
Die Studierenden können Methoden, Konzepte und Software-Werkzeuge für
die Extraktion von Daten aus Informationssystemen sowie für deren konsistente
Aufbereitung und intelligente Analyse beschreiben. Sie können charakteristische Anwendungsfälle von Business Process Intelligence (BPI) benennen und
technologische Realisierungsmöglichkeiten sowie deren Nutzen und Aufwände
bewerten. Darüber hinaus sind sie in der Lage, aktuelle Entwicklungen (z. B.
Process Mining, Process Performance Measurement) zu vergleichen.
Content:
- Data-Warehouse-Systeme: Architektur; Extraktion, Transformation und Laden
von Daten; Multidimensionales Daten-modell; Anfrageverarbeitung und optimierung, materialisierte Views
- Techniken für die Analyse von (Anwendungs-)Daten: OLAP, Data Mining
- Techniken für die Analyse von Prozessdaten: Process Mining, Conformance
Checking, Process Variants Mining
- Process Performance Measurement: Key Performance Indicators, Process Warehouse, Software-Werkzeuge
- Aktuelle Trends aus Forschung und Entwicklung, z.B. Business Process Compliance und Business Rule Engines
Literature:
- Vorlesungsskript
- Weiterführende Literatur wird in der Lehrveranstaltung bekannt gegeben.
Basis for:
Masterarbeiten zum Thema Business Process Intelligence.
Modes of learning
and teaching:
Lecture Business Process Intelligence, 2 SWS ()
Exercise Business Process Intelligence, 1 SWS ()
Laboratory Business Process Intelligence, 1 SWS ()
89
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung erfolgt in Form einer schriftlichen Klausur. Bei geringer
Teilnahme erfolgt die Modulprüfung ggf. mündlich; dies wird zu Beginn der
Veranstaltung bekannt gegeben.
Requirements
(formal):
keine
Grading:
Die Modulnote ergibt sich aus der Modulprüfung.
Bei einer erfolgreichen Teilnahme an den Übungen und am Labor wird dem
Studierenden ein Notenbonus auf die Modulprüfung bis zur nächst besseren
Zwischenstufe gewährt. Eine Notenverbesserung von 5,0 auf 4,0 ist nicht
möglich (§13 Absatz 4 der Fachspezifischen Studien- und Prüfungsordnung für
die Bachelor- und Masterstudiengänge Informatik und Medieninformatik). Die
genauen Modalitäten werden zu Beginn der Veranstaltung bekannt gegeben.
Basierend auf Rev. 1622. Letzte Änderung am 09.10.2015 um 16:42 durch mreichert.
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4.5.3 Data Mining
Token / Number:
88csy71994
English title:
Data Mining
Credits:
6 ECTS
Semester hours:
4
Language:
Deutsch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Dr. Friedhelm Schwenker
Training staff:
Dr. Friedhelm Schwenker
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Neuroinformatik
Computer Science and Media, M.Sc., Specialization Subject, Neuroinformatik
Computer Science, M.Sc., Specialization Subject, Mustererkennung
Computer Science and Media, M.Sc., Specialization Subject, Mustererkennung
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Specialization Subject
Cognitive Systems, M.Sc., Application Subject Data science
Requirements
(contentual):
Grundkenntnisse in Neuroinformatik
Learning objectives:
Die Studierenden kennen die wesentlichen Methoden und Verfahren des Data
Mining. Sie kennen die grundlegenden Methoden der uni-variaten und multivariaten Statistik und sind speziell mit den maschinellen Lernverfahren des Data
Mining zur Clusteranalyse, Klassifikation und Regression vertraut und können
diese in kleineren Aufgabenstellungen auch anwenden.
Content:
-
Uni- und multivariate statistische Verfahren
Clusteranalyseverfahren
Visualisierung und Dimensionsreduktion
Lernen von Assoziationsregeln
Klassifikationverfahren
Regrossion und Prognose
Statistische Evaluierung
Literature:
- Mitchell, Tom: Machine Learning, Mc Graw Hill, 1997
- Bishop, Chris: Pattern Recognition and Machine Learning, Springer, 2007
- Hand, David und Mannila, Heikki und Smyth, Padhraic: Principles of Data
Mining, MIT Press, 2001
- Witten, Ian H. und Frank, Eibe: Data mining, Morgan Kaufmann, 2000
- Skript zur Vorlesung, 2011
Basis for:
–
Modes of learning
and teaching:
Lecture Data Mining, 2 SWS (Dr. Friedhelm Schwenker)
Exercise Data Mining, 2 SWS (Dr. Friedhelm Schwenker)
91
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung erfolgt schriftlich.
Requirements
(formal):
Bachelor
Grading:
Die Modulnote ergibt sich aus der Modulprüfung. Bei erfolgreicher Teilnahme
an den Übungen wird dem Studierenden ein Notenbonus gemäß §13 (5) der
fachspezifischen Prüfungsordnung Informatik/Medieninformatik/Software Engineering gewährt.
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4.5.4 Information Retrieval and Web Mining
Token / Number:
88csyneu
English title:
Information Retrieval and Web Mining
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Martin Theobald
Training staff:
Prof. Dr. Martin Theobald
Integration of module
into courses of studies:
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science, M.Sc., Specialization Subject, Informationssysteme
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Software-Engineering, M.Sc., Specialization Subject, Informationssysteme
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Computer Science and Media, M.Sc., Specialization Subject, Informationssysteme
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Application Subject Data science
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Grundlagenwissen zu Stochastik, wie in der Vorlesung Angewandte Stochastik
I vermittelt, ist von Vorteil, aber nicht zwingend erforderlich. Grundkenntnisse
zu Datenbanken und Informationssystemen sind von Vorteil.
Learning objectives:
Der Kurs bietet den Studierenden folgende Lernziele:
- Die Studierenden erkennen, wie moderne Suchmaschinen funktionieren.
- Die Studierenden analysieren, welche Algorithmen der Ähnlichkeitssuche und
Ranglistengenerierung unterliegen.
- Die Studierenden analysieren, wie diese Algorithmen für die Interessen individueller Benutzer personalisiert werden können.
- Die Studierenden erkennen, wie diese Algorithmen skalierbar auf verteilte Rechnerarchitekturen abgebildet werden könnnen.
- Des Weiteren erkennen die Studierenden, wie große Webdatensammlungen zur
Klassifikation und Ähnlichkeitssuche von Dokumenten effizient analysiert werden können.
93
Content:
-
Information-Retrieval ist eine Disziplin, die zentrale Aspekte der Dokumentenverarbeitung, der automatischen Ranglistengenerierung sowie der skalierbaren
Datenanalyse miteinander verbindet. Ein Kernthema im Information-Retrieval
ist die effektive und effiziente und Bearbeitung von Stichwortanfragen. Dabei
sind moderne Verfahren im Information-Retrieval weder auf reine Stichwortanfragen noch auf Textdokumente beschränkt, sondern können zunehmend flexibel mit den verschiedensten Datenformaten sowie mit natürlichsprachlichen
Benutzeranfragen umgehen.
Der Bereich Web-Mining fokusiert auf eine Art der Informationsverarbeitung,
die unabhängig von spezifischen Benutzeranfragen nach charakteristischen
Mustern in großen Sammlungen von Webdokumenten sucht. Bekannte
Beispiele hierfür sind wohl Google’s PageRank Algorithmus oder Produktempfehlungen bei Amazon. Aktuelle Ansätze im Information-Retrieval und
Web-Mining verfolgen dabei zunehmend Techniken, die aus dem maschinellen
Lernen bzw. der automatischen Sprachverarbeitung stammen, um gezielt strukturierte Informationen aus Textinhalten zu extrahieren und in Form von semantischen Wissensrepräsentationen zu speichern. Wissensbasierte Systeme,
wie beispielsweise Google’s Knowledge Graph, greifen dabei auf reichhaltige
Wissensbasen zurück, die aus Milliarden von Webdokumenten automatisch extrahiert wurden. Zusammenfassend gliedert sich er Inhalt der Vorlesung in
folgende Punkte:
Grundlagen aus der Wahrscheinlichkeitstheorie und statistischen Modellierung.
Boolesche Auswertung von Suchanfragen und Vektorraummodell.
Probabilistische Auswertungsverfahren zur Ranglistengenerierung (ProbabilisticIR, Okapi BM25).
Personalisierte Suche mit Relevanzfeedback (Robertson/Sparck-Jones, Rocchio).
Evaluation von Suchmaschinen (Precision/Recall, MAP, NDCG, etc.).
Indexierung und effiziente Anfrageauswertung (Quit&Continue, verschiedene
Top-k Algorithmen).
Linkanalyse (PageRank, HITS, TrustRank, SpamRank).
Clustering und automatische Klassifikation von Objekten (k-NN, k-Means,
Naive Bayes, SVMs).
Informationsextraktion mit Hilfe maschineller Lernverfahren sowie Grundlegende
Techniken zur Verarbeitung natürlicher Sprache (POS-Tagging, Named-EntityDetection, Dependenzparsing).
Literature:
- Vorlesungsskript
- Introduction to Information Retrieval.
Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze.
Cambridge University Press, 2008.
http://nlp.stanford.edu/IR-book/
- Modern Information Retrieval, 2nd Ed. Ricardo Baeza-Yates, Berthier RibeiroNeto. Addison Wesley, 2011. http://www.mir2ed.org/
- Mining of Massive Datasets. Jure Leskovec, Anand Rajaraman, Jeff Ullman.
Cambridge University Press, 2011. http://www.mmds.org/
Basis for:
Weitere Vorlesungen im Kontext "‘Data Science"’. Masterarbeiten zum Thema
Information Retrieval & Web Mining.
Modes of learning
and teaching:
Lecture Information Retrieval & Web Mining, 3 SWS (
Prof. Dr. Martin Theobald)
Exercise Information Retrieval & Web Mining, 1 SWS ()
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
94
Course assessment
and exams:
Die Modulprüfung erfolgt in Form einer schriftlichen Klausur. Bei geringer
Teilnahme erfolgt die Modulprüfung ggf. mündlich; dies wird zu Beginn der
Veranstaltung bekannt gegeben.
Requirements
(formal):
Keine.
Grading:
Die Modulnote ergibt sich aus der Modulprüfung.
Bei einer erfolgreichen Teilnahme an den Übungen wird dem Studierenden
ein Notenbonus auf die Modulprüfung bis zur nächst besseren Zwischenstufe
gewährt. Eine Notenverbesserung von 5,0 auf 4,0 ist nicht möglich (§13 Absatz
4 der Fachspezifischen Studien- und Prüfungsordnung für die Bachelor- und
Masterstudiengänge Informatik und Medieninformatik). Die genauen Modalitäten werden zu Beginn der Veranstaltung bekannt gegeben.
Basierend auf Rev. 1586. Letzte Änderung am 08.10.2015 um 12:26 durch mreichert.
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4.5.5 Introduction to Data Science
Token / Number:
88csyneu
English title:
Introduction to Data Science
Credits:
6 ECTS
Semester hours:
4
Language:
English
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Martin Theobald
Training staff:
Prof. Dr. Martin Theobald
Integration of module
into courses of studies:
Computer Science, B.Sc., Main Subject,
Computer Science and Media, B.Sc., Main Subject,
Software-Engineering, B.Sc., Main Subject, Software-Engineering
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, M.Sc., Core Subject, Praktische und Angewandte
Informatik
Software-Engineering, M.Sc., Core Subject, Software Engineering
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Application Subject Data science
Cognitive Systems, M.Sc., Specialization Subject
Requirements
(contentual):
Keine.
Learning objectives:
Die Studierenden erkennen, wie die verteilte Datenverarbeitung mit aktuellen
Technologien im Bereich der KeyValue-Stores und NoSQL-Datenbanken funktioniert. Die Studenten analysieren, wie eine komplexe Anwendung mittels
dieser Systeme realisiert werden kann und implementieren auch aktiv eine solche
Anwendung im Verlauf des Kurses. Desweiteren vermittelt der Kurs einen
Überblick über die allgemeine Funktionsweise und die theoretischen Grundlagen der verteilten Datenverarbeitung.
96
Content:
Literature:
Der Begriff “Data Science” ist zu einem wichtigen Schlagwort im Umgang mit
großen Datenmengen geworden. DBIS reagiert auf diese aktuelle Entwicklung mit einer neuen Vorlesung Einführung in Data Science, in welcher den
Studierenden bereits im Bachelorstudium die Grundkonzepte der skalierbaren
Verarbeitung von großen Datenmengen in verteilten Rechnerarchitekturen vermittelt werden.
Die Vorlesung gibt Einblicke in die Funktionsweise verteilter Dateisysteme, wie
beispielsweise das verteilte Hadoop-Dateisystem (HDFS), und vermittelt den
Studierenden einen ersten, praxisorientierten Umgang im Programmieren von
verteilten Anwendungen in MapReduce. Des Weiteren ermöglicht der Kurs
einen Einblick in aktuelle Programmierschnittstellen (API’s) und Datenmodelle im sogenannten “Apache-Hadoop Ecosystem”. Dabei sammeln die Studenten ebenfalls praktische Erfahrung mit weiteren Werkzeugen im Bereich der
sogenannten KeyValue-Stores und aktuellen NoSQL-Datenbanken wie Apache
HBase, Apache HIVE, Apache SPARK und MongoDB. Vertiefende Themen
zu den theoretischen Grundlagen der verteilten Datenverarbeitung, zur Modellierung von klassischen Datenbankkonzepten mittels dieser neuen Technologien
und zur Verarbeitung verschiedener Dokumentformate wie beispielsweise Textund XML-Daten, aber auch neuer Datenformate wie JSON runden den Kurs
ab.
Die Vorlesung Einführung in Data Science gibt einen ersten Einblick die oben
genannten Technologien und zeigt diese auch im Zusammenspiel. Der Schwerpunkt liegt in der praxisorientierten Anwendung der zu Grunde liegenden Architekturen, in welcer die Studierenden anhand von wöchentlichen, aufeinander
aufbauenden Programmierübungen ein komplexes Projekt in Hadoop zu implementieren erlernen. Dabei wird auch auf die theoretischen Grundlagen dieser
Technologien eingegangen sowie ein Einblick in die internen Aspekte dieser Systeme gewährt.
- Vorlesungsskript
Basis for:
Der Kurs bietet eine ideale Basis für weitere Projekte und Vertiefungsthemen
im Bereich “Data Science”, welche von DBIS angeboten werden.
Modes of learning
and teaching:
Lecture Einführung in Data Science, 2 SWS (Prof. Martin Theobald)
Laboratory Einführung in Data Science, 2 SWS ()
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
Die Modulprüfung erfolgt in Form einer schriftlichen Klausur. Bei geringer
Teilnahme erfolgt die Modulprüfung ggf. mündlich; dies wird zu Beginn der
Veranstaltung bekannt gegeben.
Als Leistungsnachweise für das Labor sind eine praktische Problemlösung,
schriftliche Kurzberichte sowie eine Ergebnispräsentation zu erbringen.
Requirements
(formal):
Keine.
Grading:
Die Modulnote ergibt sich zu 50% aus der Modulprüfung und zu 50% aus dem
Labor.
Basierend auf Rev. 1585. Letzte Änderung am 08.10.2015 um 12:20 durch mreichert.
97
4.5.6 Project Non-Traditional Database Architectures
Token / Number:
88csyneu
English title:
Project Non-Traditional Database Architectures
Credits:
8 ECTS
Semester hours:
4
Language:
Deutsch/Englisch
Turn / Duration:
Every Semester / 1 Semester
Module authority:
Prof. Dr. Martin Theobald
Training staff:
Prof. Dr. Martin Theobald
Prof. Dr. Manfred Reichert
Integration of module
into courses of studies:
Computer Science, M.Sc., Project,
Computer Science and Media, M.Sc., Project,
Computer Science, Lehramt, Optional Module,
Cognitive Systems, M.Sc., Application Subject Data science
Requirements
(contentual):
Grundlagenwissen zu Informationssystemen, wie es z. B. in den Modulen Datenbanksysteme – Konzepte und Modelle und Data Science vermittelt wird.
Learning objectives:
Die Studierenden sind in der Lage, innovative Anwendungs- bzw. Informationssysteme auf Grundlage aktueller Datenbankarchitekturen (z.B. aus dem Bereich KeyValue-Stores, NoSQL und eigener Prototypen) zu entwickeln. Ferner
können sie eine komplexe Aufgabenstellung analysieren, verwandte Lösungen
eigenständig auswählen und ggf. transferieren sowie selbständig im Team, unter
Verwendung moderner Methoden und Werkzeuge zur Entwicklung von Datenbankarchitekturen, ein skalierbares Anwendungssystem zu entwickeln.
Sie sind weiter in der Lage, ihre Ergebnisse mit anderen Teams abzustimmen,
professionell zu dokumentieren und im Rahmen einer Präsentation überzeugend vorzustellen. Schließlich besitzen sie wichtige Schlüsselqualifikationen, wie
die Fähigkeiten sich in ein neues und komplexes Themengebiet einzuarbeiten,
Erkenntnisse aus der wissenschaftlichen Literatur aufzugreifen sowie ein Projekt
professionell abzuwickeln.
Content:
Zu Beginn des Projektes werden in einem Workshop konkrete Themenstellungen erarbeitet und diskutiert. Danach werden Teams gebildet und jedem Team
wird eine konkrete Aufgabenstellung zugewiesen, die es dann eigenständig zu
bearbeiten gilt. Am Ende des Projekts soll ein ablauffähiges Anwendungssystem stehen, das sich durch innovative Lösungen und Eigenschaften auszeichnet.
Mögliche Themen sind:
Entwurf und Realisierung eines skalierbaren Informationssystems mit MapReduce/Hadoop
Entwurf und Realisierung von eigenen Software-Prototypen im “Big Data” Bereich
Gestaltung, Entwurf und Implementierung von Visualisierungen für große Datensammlungen
Entwurf und Implementierung mobiler Dienste und Applikationen (z. B. für
iPhone, iPad, Android)
-
Literature:
Basis for:
- Literatur wird in der Lehrveranstaltung bekannt gegeben.
Das Projektmodul bietet eine fundierte Grundlage für eine Masterarbeit im
Bereich Datenbanken und Informationssysteme bzw. Data Science.
98
Modes of learning
and teaching:
Project Nicht-traditionelle Datenbankarchitekturen, 4 SWS (Prof.
Theobald)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 180 h
Sum: 240 h
Course assessment
and exams:
Anwesenheit bei den Workshops und Besprechungen mit dem Betreuer sowie aktive Mitarbeit im Projektteam wird vorausgesetzt. Die genauen Regeln werden
zu Beginn der Lehrveranstaltung bekannt gegeben. Als Leistungsnachweise sind
ein Abschlussbericht, eine Abschlusspräsentation sowie eine praktische Problemlösung zu erbringen.
Requirements
(formal):
keine
Grading:
Es werden Noten für den Abschlussbericht, die Abschlusspräsentation sowie die
praktische Problemlösung vergeben. Die Gewichtung dieser Noten ist jeweils
abhängig vom Thema und wird zu Beginn des Projekts bekannt gemacht.
Basierend auf Rev. 1596. Letzte Änderung am 08.10.2015 um 14:41 durch mreichert.
99
Martin
4.5.7 Research Trends in Data Science
Token / Number:
88csyneu
English title:
Research Trends in Data Science
Credits:
4 ECTS
Semester hours:
2
Language:
Englisch
Turn / Duration:
Every Semester / 1 Semester
Module authority:
Prof. Dr. Martin Theobald
Training staff:
Prof. Dr. Martin Theobald
Prof. Dr. Manfred Reichert
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Data science
Requirements
(contentual):
Keine
Learning objectives:
Studierende lernen anhand eines konkreten, fachbezogenen und abgegrenzten
Themas die Aufbereitung von Informationen. Sie können eine gegliederte und
mit korrekten Zitaten ausgestattete und im Umfang begrenzte Ausarbeitung
erstellen. Sie können einen freien Vortrag vor kleinem Publikum halten. Die
dazu benötigten Präsentationsmaterialien entsprechen didaktischen Maßstäben.
Studierende können sich in eine fachliche Diskussion einbringen. Sie sind in der
Lage konstruktive Kritik zu geben und entgegen zu nehmen. Sie können anhand
der vermittelten Kriterien die Darstellung anderer Vortragender bewerten und
einordnen.
Content:
Es werden fortgeschrittene Forschungsaspekte (basierend auf aktuellen wissenschaftlichen Veröffentlichungen) aus dem weitläufigen Gebiet “Data Science”
und “Big Data” bearbeitet und diskutiert. Aktuelle Themengebiete sind u.a.:
Aktuelle Architekturen von KeyValue-Stores und NoSQL-Datenbanken
Skalierbare Verarbeitung großer Datenmengen in MapReduce
Verteilte Graphdatenbanken
Probabilistische und Temporale Datenbankmodelle und Systeme
Maschinelles Lernen in verteilten Umgebungen
Visualisierung großer Datenmengen
Analyse von Sozialen Netzwerken
Literature:
- Wird bei der Themenvergabe bekannt gegeben
Basis for:
Keine
Modes of learning
and teaching:
Seminar Aktuelle Forschungstrends in Data Science ()
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
Leistungsnachweis über erfolgreiche Teilnahme. Diese umfasst Anwesenheit und
enthält Ausarbeitung, Vortrag und Mitarbeit
100
Requirements
(formal):
Keine
Grading:
unbenotet
Basierend auf Rev. 1639. Letzte Änderung am 12.10.2015 um 07:36 durch vpollex.
101
4.6 User-Centered Planning
4.6.1 Project User-Centered Planning
Token / Number:
88csynew
German title:
Projekt Nuterzentrierte Handlungsplanung
Credits:
16 ECTS
Semester hours:
8
Language:
English or German
Turn / Duration:
Sporadic / 2 Semester
Module authority:
Prof. Dr. Susanne Biundo-Stephan
Training staff:
Prof. Dr. Susanne Biundo-Stephan
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject User-centered planning
Computer Science, M.Sc., Project,
Requirements
(contentual):
Students should be familiar with basic AI techniques. Especially knowledge
about Automated Planning is helpful.
Learning objectives:
The students are capable to deal with scientific literature and to identify the
state-of-the-art in a specific field of research in artificial intelligence. They can
apply scientific methods in order to solve a task in the given context. The
students are able to design experiments involving human users, which are adequate for investigating a given hypothesis. They can implement (prototypical)
software systems that are necessary to conduct these experiments and are able
to analyze the gathered data with respect to the hypothesis.
Content:
Planning problems describe the world in an abstract way. They provide operators that define how the world can be changed and a desired goal which should
be reached. Domain-independent planning systems are capable to automatically generate solutions for any planning problem, which can be specified in
the formalism. Thus they provide a core functionality for intelligent systems.
Solutions define (1) which steps have to be executed to reach a goal and (2)
constraints on the ordering of their execution. Though they form a powerful
tool to realize intelligent behavior, there are several challenges when they are
integrated into systems that interact with human users.
In this project, students deal with challenges that occur when planning-based
systems are used in human computer interaction. One such challenge is the
explanation of generated plans to human users. Explanations allow a system to
answer questions like "Why do I have to perform this action?". Another one is
mixed-initiative planning, where planning systems interact with humans during
the planning process in order to find a plan collaboratively. A last example is
the presentation of plans to human users. This includes, e.g., determining in
which ordering the tasks are presented.
Literature:
Introductional literature will be provided at the beginning of the project.
Basis for:
-
Modes of learning
and teaching:
Project User-Centered Planning (Prof. Dr. Susanne Biundo-Stephan)
Estimation of
effort:
Active Time: 120 h
Preparation and Evaluation: 360 h
Sum: 480 h
102
Course assessment
and exams:
Students have to provide a project report, a documented software system (if
the project includes software development), and data, documentation, and analysis of the experimental evaluation (if the project includes such an evaluation).
Students have to give a final presentation.
Requirements
(formal):
None
Grading:
The mark for the module is determined as the weighted average of the grades
for report, software system/documentation, evaluation, and presentation.
Basierend auf Rev. 1550. Letzte Änderung am 17.06.2015 um 08:23 durch vpollex.
103
4.7 Semantic Web Technology
4.7.1 Project Semantic Web Technologies
Token / Number:
88csynew
German title:
Projekt Semantic Web Technologien
Credits:
16 ECTS
Semester hours:
8
Language:
English
Turn / Duration:
Sporadic (Winter Term) / 2 Semester
Module authority:
Jun.-Prof. Dr. Birte Glimm
Training staff:
Jun.-Prof. Dr. Birte Glimm
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Semantic web technology
Requirements
(contentual):
None
Learning objectives:
Students are able to conduct a project-oriented, scientific work in the area
of Semantic Web technologies. They have the ability to define an innovative
project topic, acquire skills and technologies required for the project, find related
works and base their own work on them where appropriate. The students are
further able to work independently in a team and to apply state-of-the-art
methods to develop concepts and solutions for the project topic. They can
document the results in the form of a scientific report and present their findings
in the form of a presentation. They are able to acquaint themselves with a new
topic and to conduct a scientific project.
Content:
- Assisted by the lecturer, a small team of participants develops independently a
suitable project topic in the area of the Semantic Web. Previous related work
has to be considered and incorporated into the project as required. The project
idea is documented in the form of a written detailed project proposal.
The first step within the project is the development of a theoretical concept
that considers related work in the area. As a next step, the students develop
a detailed plan for the practical realisation of the project. The projects are
conducted in small groups of 3 to 4 students. The following steps are planned
for conducting the project:
- Definition of a topic
- Literature review of related work
- Concept draft
- Evaluation of suitable technologies
- Self-learning of the required technical foundations
- Architecture design
- Development of a concrete plan for conducting the project
- Implementation
- Integration
- Test
- Deployment
- It is required to document the project. Concrete project topics are defined along
the research topics of the institute.
104
Literature:
- According to the students’ literature review
Basis for:
-
Modes of learning
and teaching:
Project Semantic Web Technologies (Jun.-Prof. Dr. Birte Glimm)
Estimation of
effort:
Active Time: 120 h
Preparation and Evaluation: 360 h
Sum: 480 h
Course assessment
and exams:
Design, implementation and evaluation of the software system; the written
project report; the system demonstration
Requirements
(formal):
Lecture Foundations of Semantic Web Technologies or Algorithms in Knowledge
Representation
Grading:
The mark of the module is determined by the implementation result, the written
project report and the system demonstration with the ration 2:1:1.
Basierend auf Rev. 1550. Letzte Änderung am 17.06.2015 um 08:23 durch vpollex.
105
4.8 Cognitive Smart Systems
4.8.1 Cognitive Smart Systems
Token / Number:
88csy?????
English title:
Cognitive Smart Systems
Credits:
16 ECTS
Semester hours:
8
Language:
Englisch
Turn / Duration:
Every Winter Term / 2 Semester
Module authority:
Prof. Dr.-Ing. Frank Slomka
Training staff:
Prof. Dr.-Ing. Frank Slomka
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive Smart Systems
Requirements
(contentual):
none
Learning objectives:
Students are developing new methods to control an autonomous underwater
vehicles. They are able to design algorithms capable of mission planning, path
finding and collision avoidance for smart robots. They know how to design
embedded hardware/software systems using platform based design. They could
implement algorithms with Matlab/Simulink using code generators to automatically synthesize hardware and software systems of an cognitive system. They
know the design flow of embedded systems and master various algorithms for
collision avoidance and data fusion. They perform autonomous research on
technology and algorithms of cognitive underwater robots and they could reflect their restlts and they are able to give an academic presentation.
Content:
During the seminar algorithmic foundations for autonomous control of underwater vehicles are being developed. This includes algorithms for collision avoidance
(sonar), navigation, location and path determination up to mission planning.
The design of the algorithms is performed model-based in the Mathwork environment Matlab / Simulink /Simscape. Algorithms may use different sensors of
the underwater vehicle. The aim is to link the teaching content of the program
Cognitive Systems with a a heterogeneous diving robotic platform.
Literature:
Basis for:
Modes of learning
and teaching:
Project Cognitive Smart Systems, 12 LP) (Prof. Dr.-Ing. Frank Slomka)
Seminar Cognitive Smart Systems (4)
LPProf. Dr.-Ing. Frank Slomka
Estimation of
effort:
Sum: 480 h
Course assessment
and exams:
Das Seminar findet erst ab mindestens 3 Teilnehmern statt. Im Seminar muss
ein wissenschaftlicher Aufsatz bearbeitet nd in einer Präsentation vorgestellt
werden. Die Projektarbeit muss in einem Bericht dokumentiert werden.
Requirements
(formal):
none
106
Grading:
Die Note für das Projekt und das Seminar wird getrennt vergeben. Die Noten
werden dann entsprechend der Leistungspunkte gewichtet.
Basierend auf Rev. 1509. Letzte Änderung am 28.05.2015 um 15:20 durch vpollex.
107
5 Interdisciplinary Subjects
5.1 Human-Computer Dialogue
5.1.1 Mobile Mensch-Computer-Interaktion
Token / Number:
88csy72013
Credits:
6 ECTS
Semester hours:
4
Language:
Deutsch, Unterlagen in Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Enrico Rukzio
Training staff:
Prof. Dr. Enrico Rukzio
Integration of module
into courses of studies:
Computer Science, B.Sc., Main Subject,
Computer Science, M.Sc., Core Subject, Praktische und Angewandte Informatik
Software-Engineering, B.Sc., Main Subject,
Software-Engineering, M.Sc., Core Subject, Praktische und Angewandte Informatik
Computer Science and Media, B.Sc., Main Subject,
Computer Science and Media, M.Sc., Core Subject, Mediale Informatik
Cognitive Systems, M.Sc., Interdisciplinary Subject Human-computer dialogue
Requirements
(contentual):
Grundlagenkenntnisse der Mensch-Computer-Interaktion und Pervasive Computing sind von Vorteil. Die relevanten Grundlagen werden für Quereinsteiger
nochmals kurz rekapituliert.
Learning objectives:
Die Studenten erlernen in dieser forschungsorientierte Lehrveranstaltung sehr
detaillierte Kenntnisse über Themenbereiche wie Interaktion mit mobilen
Endgeräten, die technischen Eigenschaften dieser Geräte (Eingabe, Ausgabe,
Sensorik), die Entwicklung interaktiver mobiler Dienste und neuartige Anwendungsbereiche. Hierbei erlernen sie insbesondere Methoden, Konzepte und
Werkzeuge bzgl. des Designs, der Entwicklung und der Evaluation entsprechender Anwendungen und Dienste unter Berücksichtigung von Aspekten wie den
limitierten Ein- und Ausgabemöglichkeiten, der Vielfalt der Nutzungskontexte
und weitere durch die Gerätegröße bedingte technische Limitationen. Die
Übung vertieft die theoretischen Aspekte durch praktische Aufgaben im Bereich der Programmierung mobiler Endgeräte mit Fokus auf mobiler MenschComputer-Interaktion, der Verwendung von Sensordaten und des Interaktionsdesign.
Content:
- Grundlegende Interaktionskonzepte: Texteingabe, Visualisierungstechniken,
taktiles Feedback, Mobile Augmented Reality, direkte und indirekte Interaktionen mit entfernten Displays, mobile Interaktion mit der realen Welt, sprachbasierte Interaktion, Wearable User Interfaces
- Technologie in mobilen Endgeräte: Sensorik (Lokation, Orientierung, Rotation,
etc.), Near Field Communication, persönliche Projektoren, Projector Phones
Literature:
- Ausgewählte Artikel von Konferenzen CHI, UIST und Mobile HCI
- Ausgewählte Artikel von Journalen / Magazinen: IEEE Pervasive Computing
und Personal and Ubiquitous Computing
- Vorlesungsskript
Basis for:
Grundlagenkenntnisse der Mensch-Computer-Interaktion und Pervasive Computing sind von Vorteil.
108
Modes of learning
and teaching:
Lecture Mobile Mensch-Computer-Interaktion, 2 SWS (Prof. Dr. Enrico
Rukzio)
Exercise Mobile Mensch-Computer-Interaktion, 2 SWS (Julian Seifert, M.Sc. /
Christian Winkler, M.Sc.)
Estimation of
effort:
Active Time: 60 h
Preparation and Evaluation: 120 h
Sum: 180 h
Course assessment
and exams:
In der Regel mündliche Prüfung, ansonsten schriftliche Prüfung von 90
minütiger Dauer.
Requirements
(formal):
Die Anmeldung zur Modulprüfung setzt keinen Leistungsnachweis voraus.
Grading:
Die Modulnote ergibt sich aus der Modulprüfung. Bei erfolgreicher Teilnahme
an den Übungen wird dem Studierenden ein Notenbonus gemäß §13 (5) der
fachspezifischen Prüfungsordnung Informatik/Medieninformatik/Software Engineering gewährt.
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5.1.2 Project - Design, Implementation and Evaluation of Dialogue Systems
Token / Number:
88csyneu
German title:
Design, Implementation und Evaluation von Dialog-Systemen
Credits:
16 ECTS
Semester hours:
2
Language:
English and German
Turn / Duration:
Every Winter Term / 2 Semester
Module authority:
Prof. Dr. Martin Baumann
Prof. Dr. Wolfgang Minker
Training staff:
Prof. Dr. Martin Baumann
Prof. Dr. Wolfgang Minker
Members of the Institute of Communication Technology and Members of the
Institute of Psychology and Education, Dep. Human Factors
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Human-computer dialogue
Requirements
(contentual):
None. No prerequisites from other lectures required.
Learning objectives:
The students show a practical understanding of multimodal spoken dialogue
systems technology through all development phases: design, implementation
and evaluation, basics of a human-centered design process, and relevant psychological theories. They are aware of the interdisciplinarity of the research
field. They apply their acquired knowledge through project-oriented practical
work.
Content:
In the framework of spoken dialogue systems development and human factor
studies several individual topics are proposed as practical studies. They may
depend on the current research topics at the Dialogue Systems Group and the
Human Factors Department.
Two students will work in a group with respective backgrounds in computer
science and psychology. Based on human-centred design appraoch a concept
of dialogue system will developed, implemented and evaluated over the course
of the two semesters.
Literature:
To be distributed during the project
Basis for:
Modes of learning
and teaching:
Project Design, Implementation and Evaluation of Dialogue Systems (Profs.
Dres. Martin Baumann and Wolfgang Minker)
Estimation of
effort:
Active Time: 180 h
Preparation and Evaluation: 300 h
Sum: 480 h
Course assessment
and exams:
Certificate after fulfilment of the following criteria: at least three meetings with
the supervisors to discuss progress of work; presentation of a course topic in the
seminar part; final presentation (demo) and discussion; short description and
illustration (max. 20 pages); submission of the final version (hard/software and
documentation) of the project to the supervisors.
110
Requirements
(formal):
None
Grading:
The mark for the module is determined by the average mark for the project
report and the written documents (slides, summary) of the seminar presentation.
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5.1.3 Psychology of Automation
Token / Number:
88csyneu
German title:
Psychology of Automation
Credits:
4 ECTS
Semester hours:
2
Language:
English and German
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Martin Baumann
Training staff:
Prof. Dr. Martin Baumann
Dr.-Ing. Nicola Fricke
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive ergonomics
Cognitive Systems, M.Sc., Interdisciplinary Subject Human-computer dialogue
Requirements
(contentual):
None.
Learning objectives:
The students possess a deep knowledge about important theoretical concepts,
methodological approaches and empirical findings in the area of human-machine
automation. They are able to apply this knowledge to successfully analyze and
solve practically relevant problems.
Content:
During the course the students will learn various definitions and taxonomies
of automation. The will become acquainted with basic concepts, applications,
impacts and drawbacks of automation in human-machine systems. This includes ironies of automation, degrees of automation and specific approaches,
e.g. adaptive automation, human-centered automation, human-machine cooperation. The students will learn about various effects of automation and
about related concepts such as situation awareness, trust and workload, research findings in the application fields medical domain, aviation and surface
transportation.
Literature:
Will be announced at the beginning of the course
Basis for:
Cognitive Systems, M.Sc., Application Subject: Cognitive Ergonomics; Cognitive Systems, M.Sc., Interdisciplinary Subject: Human-Computer Dialogue
Modes of learning
and teaching:
Seminar Psychology of Automation (Dr.-Ing. Nicola Fricke, Prof. Dr. Martin
Baumann)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The students will have to continually participate, present a topic of the field
orally and provide the presentation in written form. The exact form will be
announced in the course. Additionally some exercises might be assigend.
Requirements
(formal):
None
Grading:
The mark for the module is determined by an unweighted average of the marks
for presentation, the written documents of the presentation and if assigned of
the exercises.
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5.1.4 Transportation Human Factors
Token / Number:
88csyneu
German title:
Transportation Human Factors
Credits:
4 ECTS
Semester hours:
2
Language:
English and German
Turn / Duration:
Sporadic (Summer Term 2016) / 1 Semester
Module authority:
Prof. Dr. Martin Baumann
Training staff:
Prof. Dr. Martin Baumann
Members of the Insitute of Psychology and Education, Dept. Human Factors
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive ergonomics
Cognitive Systems, M.Sc., Interdisciplinary Subject Human-computer dialogue
Requirements
(contentual):
None.
Learning objectives:
The students possess a deep knowledge about important theoretical concepts,
methodological approaches and empirical findings in the area of transportation
human factors. The are able to apply this knowledge to successfully analyze
and solve practically relevant problems.
Content:
During the course the students will become acquainted with essential issues,
methods and theoretical concepts of the domain of transportation human factors. These include methodological approaches in the analysis of driver behaviour, psychological foundations of driver behaviour, the effect of driver assistance systems on driver behaviour and current issues in the design of drivervehicle interaction.
Literature:
Will be announced at the beginning of the course
Basis for:
Cognitive Systems, M.Sc., Application Subject: Cognitive Ergonomics; Cognitive Systems, M.Sc., Interdisciplinary Subject: Human-Computer Dialogue
Modes of learning
and teaching:
Seminar Transportation Human Factors (Prof. Dr. Martin Baumann or Members
of the Institute of Psychology and Education, Dept. Human Factors)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The students will have to continually participate, present a topic of the field
orally and provide the presentation in written form. The exact form will be
announced in the course. Additionally some exercises might be assigend.
Requirements
(formal):
None
Grading:
The mark for the module is determined by an unweighted average of the marks
for presentation, the written documents of the presentation and if assigned of
the exercises.
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5.2 Cognitive Modelling
5.2.1 Cognitive Modelling for Computer Scientists and Psychologists
Token / Number:
88csy71918
German title:
Kognitive Modellierung für Informatiker und Psychologen
Credits:
4 ECTS
Semester hours:
2
Language:
English
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Jun.-Prof. Dr. Birte Glimm
Training staff:
Dr. Marvin Schiller
Dr. Florian Schmitz
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive modelling
Computer Science, B.Sc., Seminar,
Computer Science, M.Sc., Seminar,
Computer Science and Media, B.Sc., Seminar,
Computer Science and Media, M.Sc., Seminar,
Requirements
(contentual):
None
Learning objectives:
The participants learn to deal with a scientific topic in an autonomous manner.
This includes surveying the relevant literature, identifying and evaluating the
relevant information and presenting it both in the form of an oral presentation
and a term paper. Thereby, the participants acquire skills to work the available
material into a presentation, to give the presentation in front of an audience
and to discuss questions pertaining to the topic, in addition to acquiring skills
of scientific writing. In particular, the seminar aims to provide a differentiated
overview of the various methods of cognitive modeling and their application.
Content:
Cognitive modeling is a central discipline at the intersection of cognitive science, psychology and computer science. This discipline uses formal, mathematical and computer models to investigate fundamental principles of cognitive
processes and behavior. By comparing data generated by these models with
empirical data, the adequacy of the models’ theoretical underpinnings can be
tested. Furthermore, model-specific parameters can be used to provide a differentiated description of cognitive processes and help to better understand
effects attributed to situational factors and individual differences. The seminar
encompasses a range of methods used in cognitive modelling, cognitive architectures and the modeling of different cognitive sub-systems (memory, attention,
emotion, attitude, decision making, logical reasoning). Furthermore, the use of
these methods in different application areas will be discussed.
Literature:
- To be announced in the first class (together with the assignment of topics)
Basis for:
–
Modes of learning
and teaching:
Seminar Cognitive Modeling (Dr. Marvin Schiller, Dr. Florian Schmitz)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
115
Course assessment
and exams:
Continued participation in all classes; preparation of a term paper; giving an
oral presentation
Requirements
(formal):
None
Grading:
The module is ungraded.
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116
5.2.2 Computational Psychology
Token / Number:
88csyneu
German title:
Computational Psychology
Credits:
4 ECTS
Semester hours:
2
Language:
English
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Dipl.-Ing. Thomas Frühwirth
Training staff:
Prof. Dr. Dipl.-Ing. Thomas Frühwirth
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive modelling
Requirements
(contentual):
Basic knowledge of logic (and Prolog) are advantageous.
Learning objectives:
The participants learn to work on a scientific topic autonomously. This includes
surveying the relevant literature, identifying and evaluating the relevant information and presenting it both in the form of an oral presentation and a term
paper. Thereby, the participants acquire skills to work the available material
into a presentation, to give the presentation in front of an audience and to
discuss questions pertaining to the topic, in addition to acquiring skills of scientific writing. In particular, the students get an overview of the methods of
(computational) cognitive modeling, applications and example models.
Content:
Computational Psychology is a research area at the interface of computer science and psychology. Psychological models of cognitive tasks like memory,
vision or deduction are implemented, analyzed and simulated. The results of
those simulations are compared to results from experimental psychology to
achieve a better understanding of the cognitive processes.
The seminar enables the students to get to know, analyze and evaluate new
research approaches. It deepens the presentation skills of scientific contents by
acquiration, written and oral presentation and discussion of selected literature:
Starting from given literature, the students get to know their assigned topic, do
a literature research, write a report, give a scientific talk and participate in the
discussion of other talks.
Topics are from the field of (computational) cognitive modeling and cover for
instance cognitive architectures, interesting example models, problems with current approaches in the field, analysis of cognitive models and extensions of
existing architectures.
ILIAS:
If sufficient demand exists.
Literature:
- Original literature from the field of cognitive science will be given together with
the topics of the presentations.
Basis for:
Modes of learning
and teaching:
Seminar Computational Psychology (Prof. Dr. Thomas Frühwirth)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
117
Course assessment
and exams:
Continued participation in all classes, written and oral presentation of a topic
from the field of computational psychology, participation in discussions
Requirements
(formal):
none
Grading:
The module is ungraded.
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5.2.3 Project Cognitive Modelling
Token / Number:
88csyneu
German title:
Project Cognitive Modelling
Credits:
8 ECTS
Semester hours:
4
Language:
English
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Dipl.-Ing. Thomas Frühwirth
Training staff:
Prof. Dr. Dipl.-Ing. Thomas Frühwirth
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive modelling
Requirements
(contentual):
Basic knowledge of Prolog or CHR are advantageous.
Learning objectives:
The knowledge from courses related to the topic is applied and deepened in a
practical substantial project. The students are able to apply and connect methods from cognitive modelling and (logic/constraint/imperative) programming
to solve a problem from the field of computational psychology.
Content:
Computational Psychology is a research area at the interface of computer science and psychology. Psychological models of cognitive tasks like memory,
vision or deduction are implemented, analyzed and simulated. The results of
those simulations are compared to results from experimental psychology to
achieve a better understanding of the cognitive processes.
The project deals with a popular production rule system used in cognitive sciences: ACT-R. It is the implementation of a rule-based theory of human cognition. There are numerous implementations of ACT-R and cognitive models
using ACT-R that are accompanied by scientific papers and online documentations.
With Constraint Handling Rules (CHR) there exists a powerful rule-based approach that also enables to implement ACT-R models. The benefit of using
CHR in cognitive modelling is its clear semantics, its close relation to first order
logic and theoretical results for analysis. There is a basic implementation of
ACT-R in CHR that contains a compiler from ACT-R models to CHR rules.
In the project, the students extend the existing implementation or implement
example models in CHR.
Literature:
Basis for:
Modes of learning
and teaching:
Project Cognitive Modelling (Prof. Dr. Thomas Frühwirth)
Estimation of
effort:
Active Time: 90 h
Preparation and Evaluation: 150 h
Sum: 240 h
119
Course assessment
and exams:
Quality of implementation, active participation in meetings and discussions with
supervisor (and possibly team members), written report (planning phase and
final documentation) and oral presentation. The concrete criteria are announced
at the beginning of the project.
Requirements
(formal):
none
Grading:
The module is graded.
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120
5.3 Cognitive Neuroscience - Experimental and Modeling Perspectives
5.3.1 Body and Mind: philosophisch–wissenschaftstheoretische Grundlagen der Cognitive and Neuro
Sciences
Token / Number:
88csyneu
German title:
Credits:
4 ECTS
Semester hours:
2
Language:
Deutsch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Jörg Wernecke
Training staff:
Prof. Dr. Jörg Wernecke
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive Neuroscience
Requirements
(contentual):
None
Learning objectives:
Erwerb von Wissen hinsichtlich der grundlegenden Modelle von Denken, Erkennen und Bewusstsein in einem interdisziplinären Kontext; Erwerb von analytischen Kompetenzen im Hinblick auf die Zuordnung von unterschiedlichen kognitiven Modellen und wissenschaftstheoretischen Positionen. Erwerb von Transferwissen zur Einordnung aktueller kognitiver Modelle im Hinblick auf deren
wissenschaftstheoretische Implikationen und heuristischen Leistungsfähigkeit.
Content:
- Historisch–systematischer Überblick zum Leib-Seele-Problem, zu Bewusstseins–
und Erkenntnistheorien (Materialismus, Panpsychismus, Dualismus, Identitẗstheorie usw.)
- Erkenntnis– und wissenschaftstheoretische Grundlagen der Cognitive and Neuro
Sciences (empiristischer versus phänomenologischer Zugang, kybernetischer,
informationstheoretischer, biologischer, konstruktivistischer Zugang; linguistic
und pragmatic turn; Reichweite von Modellierungen)
- Aktuelle Modelle der Cognitive and Neuro Sciences und deren wissenschaftstheoretische Implikationen (interdisziplinr̈er Zugang: Informatik, Biologie, Psychologie, Philosophie)
Literature:
-
Selected literature. More references to the literature will be given during the
course.
Ansgar Beckermann: Das Leib–Seele–Problem. Eine Einführung in die Philosophie des Geistes, Paderbron, 2011
Thomas Nagel: Geist und Kosmos, Frankfurt a.M. (5)2014
Albert Newen: Philosophie des Geistes. Eine Einfḧrung, München 2013
Alex Sutter: Göttliche Maschinen. Die Automaten fr̈ Lebendiges, Frankfurt
a.M. 1988
Max Urchs: Maschine, Körper, Geist. Eine Einführung in die Kognitionswissenschaft, Frankfurt a.M. 2002
Basis for:
Grundkenntnisse aktueller Modelle der Cognitive Science
Modes of learning
and teaching:
Seminar Mind and Body (PD Dr. Jörg Wernecke)
121
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
Individuelle Leistung — mündlich (Präsentation) oder schriftlich (Hausarbeit)
Requirements
(formal):
None
Grading:
mündliche oder schriftliche Leistung wird benotet
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122
5.3.2 Cognitive and Neural Systems
Token / Number:
88csy???
English title:
Cognitive and Neural Systems
Credits:
6 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Sporadic (Winter Term) / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive Neuroscience
Requirements
(contentual):
Mathematical basics, core cognitive systems skills
Learning objectives:
The students are introduced to the mechanisms underlying the information processing in cognitive systems, with a focus on behavioral neurosciences and the
general principles in modeling (professional competence). They are able to
mathematically describe and model processes and experimentally qualified phenomena and to analyze such models using specific tools. For the modeling of
dynamical, specifically non-linear, processes students acquire knowledge about
numerical simulation methods. In addition, they are able to make use of methods for the mathematical analysis at different levels of abstraction (methodological expertise). The students are able to design and analyze models for
complex cognitive systems, design new models, to simulate them and evaluate
their results (transfer and evaluation competence).
Content:
-
Literature:
Basis for:
Introduction - Modeling and concepts
Mathematical tools
Mathematical modeling of physical systems
Numerical methods
Linear dynamical systems - Dynamic behavior, transformations and control
Neuron models - State dynamics and computation
Circuits, networks, and systems
Non-linear dynamical systems and control
Models of learning and memory
Examples of neural modeling cognitive mechanisms
The following literature list defines a reference. Further hints to specific literature are given at the beginning of the course program:
D.H. Ballard: An Introduction to Natural Computation. MIT Press, 1997
P.S. Churchland, T.J. Sejnowski: The Computational Brain. MIT Press, 1999
E.M. Izhikevich: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. MIT Press, 2005
S. Sastry: Nonlinear Systems - Analysis, Stability, and Control. Springer, 1999
H.R. Wilson: Spikes, Decisions, and Actions. Oxford Univ. Press, 1999/2005
–
123
Modes of learning
and teaching:
Lecture (2 SWS) (Prof. Heiko Neumann)
Exercise (1 SWS) (Prof. Heiko Neumann)
Estimation of
effort:
Active Time: (Vorlesung) 45 h
Preparation and Evaluation: 135 h
Sum: 180 h
Course assessment
and exams:
The exams are oral or written depending on the number of participants. The
exact modes are announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade bonus
according to §13 (5) of the study and exam regulations in cognitive systems is
given if the exercise class is passed successfully.
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5.3.3 Project Psychophysical investigation of functions in perception, cognition and motor behavior
Token / Number:
88csy???
English title:
Project Psychophysical investigation of functions in perception, cognition and
motor behavior
Credits:
8 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. N.N.
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive Neuroscience
Requirements
(contentual):
Basic knowledge in psychophysical methodology, experiment design, and testing
are a prerequisite. In addition, programming skills are mandatory in order to design and run an experiment with subjects involved. It is necessary that students
have successfully passed the lecture on "Psychophysics" or a related course.
Knowledge concerning different psychophysical software tools are beneficial.
Learning objectives:
The students are able to identify experimental variables and select proper methods to conduct experimental tests in a larger project (professional competence).
They become acquainted in a practical experiment with basic methods, statistical tests, and evaluation techniques (methodological expertise). The students
are able to design (including programming and fully executing software-based
tests) and conduct experiments in various domains and evaluate and interpret
the results of a larger study (transfer and evaluation competence).
Content:
Experimental psychophysical investigations will be conducted in the field of cognitive systems in general, with specific topics that focus on perception, higherlevel cognitive-behavioral tasks, and motor-related behavior. This project can
be conducted in accordance with two different specifications: (i) It can be conducted as a joint project that is supervised by academic partners from different
disciplines (topics and supervisors will be announced); (ii) alternatively, it can
be conducted as a project at a specific industrial research lab or division of a
company which focuses on interdisciplinary experimental work. Inquiries should
be first sent to the module authority and then the potential options will be
forwarded and further detailed. Potential partner labs will be announced such
that students can start planning at the earliest convenience. Options for the
industrial projects might be limited and subject to certain skill requirements.
Literature:
A list of basic material will be distributed at the beginning of the first project
phase.
Basis for:
–
Modes of learning
and teaching:
Project Project Psychophysical investigation of functions in perception, cognition and motor behavior ()
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 210 h
Sum: 240 h
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Course assessment
and exams:
The successful participation and accomplishment of the project is demonstrated
through active participation and regular report to the supervisors, a final oral
presentation of the project work and results, and a written report. The report
includes a documentation of the evaluation results and proper statistical testing.
Requirements
(formal):
Bachelor
Grading:
The mark for the module is determined by the average of the mark given for
the final written project report and the final oral project presentation.
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5.3.4 Project and Seminar Visual Information Processing
Token / Number:
88csyneu
English title:
Project and Seminar Visual Information Processing
Credits:
8 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Semester / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Application Subject Cognitive Vision
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive Neuroscience
Requirements
(contentual):
Assumes basic knowledge regarding methods and principles in digital image
processing or computational vision.
Learning objectives:
The students are capable to deal with scientific technical literature and apply
scientific methodology for their studies. Students can search and analyze specific literature to present the core contents in a seminar presentation. These
investigations serve as the basis for subsequent design and implementation of
the computational mechanisms as described in the source references. They will
finally test and present the key results of their model implementations.
Content:
A specific topic from the area of cognitive systems is selected and relevant core
literature based on original scientific papers will be discussed. Students will
present the contents and core themes in a seminar talk. Based on this presentation and literature study the content of such literature study will be specified,
implemented and tested. A demonstration together with a final presentation
completes the project part. A written report is finally delivered.
Literature:
The literature that is suited for the seminar and project will be selected in regard
to the the selected topic.
Basis for:
Lectures from the core topic and specialization areas in Cognitive Systems
should be attended to utilize a sound background for application and interdisciplinary work.
Modes of learning
and teaching:
Project Seminar (2 SWS) (Prof. Heiko Neumann)
This module combines seminar and project parts. The introductory part with
literature study and preparation of scientific presentation defines the seminar.
The subsequent specification of the algorithms, implementation and evaluation
defines the project part. The details concerning the contents and the specific
time table are presented to the attendees at the beginning of the semester.
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 210 h
Sum: 240 h
Course assessment
and exams:
The final mark will be determined through the initial seminar presentation, the
final project presentation, and the project report. A minimum achievement for
all three parts is mandatory.
Requirements
(formal):
None
127
Grading:
The mark for the module is determined by the average mark obtained for the
seminar presentation, final project presentation and the project report. Each
one of the three partial contributions is graded. The final mark is determined
by the average of equally weighted grades.
Basierend auf Rev. 1590. Letzte Änderung am 08.10.2015 um 13:07 durch mreichert.
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5.3.5 Psychophysics - Methods, Paradigms, and Experimentation
Token / Number:
88csy???
English title:
Psychophysics - Methods, Paradigms, and Experimentation
Credits:
4 ECTS
Semester hours:
3
Language:
Englisch
Turn / Duration:
Sporadic (Summer Term) / 1 Semester
Module authority:
Prof. Heiko Neumann
Training staff:
Prof. Heiko Neumann
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive Neuroscience
Requirements
(contentual):
None
Learning objectives:
The students are introduced to the history and foundation of experimental techniques in behavioral neuroscience, namely (visual) psychophysics. They are able
to identify experimental variables and experimental methods to conduct experimental tests (professional competence). In addition, they become acquainted
with basic methods, statistical tests, and evaluation techniques (methodological expertise). The students are able to design and conduct experiments in
various domains and evaluate and interpret the results (transfer and evaluation
competence).
Content:
-
Introduction - What is psychophysics?
Detection thresholds, psychometric functions
Example experiment
Psychophysical procedures & signal detection
Adaptive methods
The MATLAB psychophysics toolbox
Statistical analysis - Selected tests and methods
Selected behavioral data acquisition methods
Other psychophysics tools – PsychoPy, mobile psychophysics
Exercises serve as a means for implementation and practical testing of the
techniques and methods discussed in the lecture.
Literature:
The following literature list defines a reference. Further hints to specific literature are given at the beginning of the course program:
- F.A.A. Kingdom, N. Prins: Psychophysics - A Practical Introduction. Academic
Press, 2010
- D.A. Rosenbaum: MATLAB for Behavioral Scientists. Psychology Press, 2007
- E.B. Goldstein: Sensation and Perception. Thomson Publ., 2002
Basis for:
–
Modes of learning
and teaching:
Lecture (2 SWS) (Prof. Heiko Neumann)
Exercise (1 SWS) (Georg Layher)
Estimation of
effort:
Active Time: (Vorlesung) 45 h
Preparation and Evaluation: 135 h
Sum: 180 h
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Course assessment
and exams:
The exams are oral or written depending on the number of participants. The
exact modes are announced in the lecture.
Requirements
(formal):
None
Grading:
The mark for the module is determined by the exam mark. A grade bonus
according to §13 (5) of the study and exam regulations in cognitive systems is
given if the exercise class is passed successfully.
Basierend auf Rev. 1492. Letzte Änderung am 22.04.2015 um 13:21 durch vpollex.
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5.3.6 Seminar Computational modeling of cognitive functions
Token / Number:
88csy71951
German title:
Seminar Modellierung kognitiver Funktionen
Credits:
4 ECTS
Semester hours:
2
Language:
English
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Günther Palm
Training staff:
Prof. Dr. Günther Palm
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive Modeling
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive Neuroscience
Requirements
(contentual):
None
Learning objectives:
The students are able to understand scientific literature in cognitive science and
to grasp advanced ideas and methodical approaches. A critical discussion of
the contents is presented in written form and in a scientific talk.
Content:
-
Topics of
cognition
cognition
cognition
cognition
cognition
cognitive science, e.g.:
and the brain
and emotion
and language
and learning
and problem solving.
Literature:
Original literature from the field of cognitive science will be given together with
the topics of the presentations.
Basis for:
None
Modes of learning
and teaching:
Seminar Computational modeling of cognitive functions, 2 SWS (Prof. Dr. Günther Palm)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
Written and oral presentation of a topic from the field of cognitive sciene
Requirements
(formal):
None
Grading:
No mark is given for this modul.
Basierend auf Rev. 1556. Letzte Änderung am 19.06.2015 um 16:35 durch hneumann.
131
5.3.7 Thinking about Science
Token / Number:
88csyneu
German title:
Nachdenken über Wissenschaft
Credits:
4 ECTS
Semester hours:
2
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Hans–Peter Eckle
Prof. Dr. Renate Breuninger
Training staff:
Prof. Dr. Hans–Peter Eckle
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive Neuroscience
Requirements
(contentual):
None
Learning objectives:
The students will acquire an understanding of the major debates in the meta–
scientific disciplines: philosophy, history, ethics, and sociology of science. From
these meta–scientific vantage points and in addition to the criteria of their
scientific discipline, they will be able to develop critical and self–critical criteria
to put into perspective their own work and developments in their science and
in science in general. In particular, their awareness will be raised to the issues
of “Good Scientific Practice", i.e. the ethical behaviour within science and how
these issues arise in practice contexts.
Content:
- Philosophy: Why philosophy?, Philosophy in action
- Philosophy of science: Why care?
- History of science and of the thinking about science: From the ancients to the
Scientific Revolution
- History of science and of the thinking about science: From the Scientific Revolution to the present
- Key issues in the philosophy, history, ethics, and sociology of science
- Empiricism versus rationalism
- Inductivism, deductivism, critical rationalism, paradigm theory, recent developments
- Rules, norms, and values of science
- Research ethics
Literature:
Selected literature. More references to the literature will be given during the
course.
- Kent W. Staley: An Introduction to the Philosophy of Science, Cambridge
University Press, 2014
- Peter Godfrey–Smith: Theory and Reality — An Introduction to the Philosophy
of Science, University of Chicago Press, 2003
- Robert Klee: Introduction to the Philosophy of Science — Cutting Nature at
its Seams, Oxford University Press, 1997
Basis for:
The module provides general perspectives of reflection on science which are
useful to inform any scientific activity.
132
Modes of learning
and teaching:
Seminar Thinking about Science (Dr. Hans–Peter Eckle)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The exam is oral (in the form of a presentation) or written (in the form of a
take home essay).
Requirements
(formal):
None
Grading:
Presentation or essay will be graded.
Basierend auf Rev. 1551. Letzte Änderung am 17.06.2015 um 09:07 durch vpollex.
133
5.3.8 Topics in Cognitive Neuroscience
Token / Number:
88csy
English title:
Topics in Cognitive Neuroscience
Credits:
4 ECTS
Semester hours:
2
Language:
Englisch
Turn / Duration:
Sporadic (Winter Term) / 1 Semester
Module authority:
Prof. Dr. Heiko Neumann
Training staff:
Prof. Dr. Heiko Neumann
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive Neuroscience
Requirements
(contentual):
None, knowledge acquired from successful participation of a lecture in computational vision or related topic is advantageous.
Learning objectives:
The students acquire basic knowledge of scientific work and searching for and
dealing with scientific literature (evaluation competencies). They are able to
analyze specific literature and to develop strategies to search for additional
literature from selected sources, extract the key messages, analyze them and
evaluate them (evaluation and presentation competencies). The students practice scientific discourse and discussion of scientific content.
Content:
The seminar discusses a focus theme and basic literature is selected. Such
literature will be presented in the beginning and the students select a particular
topic. After the introductory reading phase in the seminar additional literature
will be selected and studied subsequently (literature search). The students
prepare and present their theme in a ’spotlight’. A summary document will
be prepared and distributed among the other participants. A final extended
seminar presentation is delivered, each of which will be discussed in the greater
audience. The written summary papers serve as basis to prepare the discussion.
Literature:
A set of papers will be selected with reference to the specific thematic focus of
the seminar.
Basis for:
–
Modes of learning
and teaching:
Seminar Topics in Cognitive Neuroscience (
Prof. Dr. Heiko Neumann)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The final mark will be determined through the initial ’spotlight’ presentation,
the final seminar presentation, and the seminar report. A minimum achievement
for all three parts are are mandatory.
Requirements
(formal):
None
134
Grading:
The mark for the module is determined by the average mark obtained for the
’spotlight’ presentation, the final seminar presentation and the seminar report.
The final mark is determined by the average of equally weighted grades given
for each of the partial fulfillments.
Basierend auf Rev. 1551. Letzte Änderung am 17.06.2015 um 09:07 durch vpollex.
135
5.4 Cognitive Agents, Companions, and Cognitive Apps
5.4.1 Cognitive Agents, Companions, and Mobile Apps in Healthcare
Token / Number:
88csy
English title:
Cognitive Agents, Companions, and Mobile Apps in Healthcare
Credits:
4 ECTS
Semester hours:
2
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Manfred Reichert
Training staff:
Prof. Dr. Manfred Reichert
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive agents
Requirements
(contentual):
None.
Learning objectives:
The students acquire basic knowledge in searching for and dealing with scientific literature (evaluation competencies). They are able to analyze specific
literature as well as to develop strategies to search for additional literature from
selected sources, extract the key messages, analyze the papers, and evaluate
them (evaluation and presentation competencies). The students practice scientific discourse and discussion of scientific content.
Content:
The colloquium discusses sophisticated topics related to cognitive software applications. In particular, the focus is on cognitive agents, companions, and
mobile apps in healthcare. At the beginning of the colloquium, basic literature will be presented and the students select a particular topic. After the
introductory reading phase, additional literature will be selected and studied
subsequently (literature search). The students prepare and present their theme
in a ’spotlight’. A summary document will be prepared and distributed among
the other participants. A final extended colloquium presentation is delivered,
each of which will be discussed in the greater audience. The written summary
papers serve as basis to prepare the discussion.
Literature:
A set of papers will be selected with reference to the specific thematic focus of
the seminar.
Basis for:
–
Modes of learning
and teaching:
Seminar Cognitive Agents, Companions, and Mobile Apps in Healthcare (
Prof. Dr. Manfred Reichert)
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 90 h
Sum: 120 h
Course assessment
and exams:
The final mark will be determined through the initial ’spotlight’ presentation,
the final seminar presentation, and the seminar report. A minimum achievement
for all three parts are mandatory.
Requirements
(formal):
None
136
Grading:
The mark for the module is determined by the average mark obtained for the
’spotlight’ presentation, the final seminar presentation, and the seminar report.
The final mark is determined by the average of equally weighted grades given
for each of the partial fulfillments.
Basierend auf Rev. 1578. Letzte Änderung am 08.10.2015 um 08:48 durch mreichert.
137
5.4.2 Project Cognitive Solutions for Mobile Guidance, Assessment and Crowd Sensing
Token / Number:
88csy???
English title:
Project Cognitive Solutions for Mobile Guidance, Assessment and Crowd Sensing
Credits:
8 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Winter Term / 1 Semester
Module authority:
Prof. Dr. Manfred Reichert
Training staff:
Prof. Dr. Manfred Reichert
Prof. Dr. Iris Kolassa
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive agents
Requirements
(contentual):
Learning objectives:
The students are able to elicit the fundamental requirements of a cognitive
agent, companion or app for a particular application scenario with a specific
focus on mobile user guidance, mobile assessment and/or mobile data collection.
The students can apply fundamental design principles for cognitive systems to
properly develop an agent, companion or app that meets the requirements of
the considered application scenario properly. Furthermore, they are able to
demonstrate the practical benefit of this cognitive system as well as to discuss
its strengths and limitations with domain experts.
Content:
Cognitive mobile agents, companions, and apps shall assist humans in both
professional and private life. In practice, a wide range of use cases in various
application domains (e.g. healthcare, logistics, sports) can be supported by
such cognitive systems. In the project, the students will investigate a particular
application scenario and elaborate alternative ways of enabling mobile user guidance, assessment and/or data collection for it. Furthermore, they will design
proper user interfaces and interaction concepts for these alternatives and then
implement a cognitive mobile agent, companion or app to demonstrate one of
them in detail.
Literature:
A list of basic material will be distributed at the beginning of the first project
phase.
Basis for:
–
Modes of learning
and teaching:
Project Project Cognitive Solutions for Mobile Guidance, Assessment and Crowd
Sensing ()
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 210 h
Sum: 240 h
Course assessment
and exams:
The successful participation and accomplishment of the project is demonstrated
through active participation and regular report to the supervisors, a final oral
presentation of the project work and results, and a written report. The report
includes a documentation of the evaluation results and proper statistical testing.
138
Requirements
(formal):
Bachelor
Grading:
The mark for the module is determined by the average of the mark given for
the final written project report and the final oral project presentation.
Basierend auf Rev. 1639. Letzte Änderung am 12.10.2015 um 07:36 durch vpollex.
139
5.4.3 Project Inreasing Patient Engagement through Cognitive Companions and Apps
Token / Number:
88csy???
English title:
Project Inreasing Patient Engagement through Cognitive Companions and Apps
Credits:
8 ECTS
Semester hours:
4
Language:
Englisch
Turn / Duration:
Every Summer Term / 1 Semester
Module authority:
Prof. Dr. Manfred Reichert
Training staff:
Prof. Dr. Manfred Reichert
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Interdisciplinary Subject Cognitive agents
Requirements
(contentual):
Learning objectives:
The students are able to elicit the requirements for cognitive companions, mobile health apps, and fitness tracking devices that increase the engagement of
patients with their health and wellness. The students can further apply fundamental principles and concepts of cognitive systems in order to properly design
and implement sophisticated user interfaces and interaction concepts that meet
these requirements. They can demonstrate the practical use of the realized patient companion or mobile app, and are familiar with its opportunities and
limitations in respect to patient engagement.
Content:
Cognitive companions, mobile health apps, and fitness tracking devices all play
a role in ensuring that patients become engaged with their health and overall
wellness. Whether it is to remain dedicated to manage one’s medications and
prescriptions or to adhere to one’s diet and exercise routine, mobile health
apps and technologies can be used to boost patient engagement with health
and wellness. However, various challenges need to be tackled by providers to
incorporate these apps and devices into the patients’ lifestyles. While patients
are usually willing to use, for example, mobile health apps as a part of their
healthcare, it is critical for developers to make these apps holistic and easy
to use for the patients. By investigating a concrete healthcare or well-being
scenario, the students will learn how to design mobile health apps and other
Internet-based, remote technologies in a way that increases the engagement of
patients with their healthcare and assists providers in meeting meaningful use
objectives.
Literature:
A list of basic material will be distributed at the beginning of the first project
phase.
Basis for:
Modes of learning
and teaching:
Project Project Inreasing Patient Engagement Through Cognitive Companions
and Apps ()
Estimation of
effort:
Active Time: 30 h
Preparation and Evaluation: 210 h
Sum: 240 h
140
Course assessment
and exams:
The successful participation and accomplishment of the project is demonstrated
through active participation and regular report to the supervisors, a final oral
presentation of the project work and results, and a written report. The report
includes a documentation of the evaluation results and proper statistical testing.
Requirements
(formal):
Bachelor
Grading:
The mark for the module is determined by the average of the mark given for
the final written project report and the final oral project presentation.
Basierend auf Rev. 1639. Letzte Änderung am 12.10.2015 um 07:36 durch vpollex.
141
6 Final Thesis
6.1 Master’s Thesis
Token / Number:
88csy80000
German title:
Masterarbeit
Credits:
30 ECTS
Semester hours:
0
Language:
English
Turn / Duration:
Every Semester / 1 Semester
Module authority:
Prof. Dr. Manfred Reichert (Studiendekan)
Training staff:
Primary supervisor of the thesis
Integration of module
into courses of studies:
Cognitive Systems, M.Sc., Thesis Masterarbeit
Requirements
(contentual):
Desired: specialized elective modules in the scientific area of the thesis
Learning objectives:
Acquisition and demonstration of the following competencies:
- Independent treatment of complex problems, using the skills acquired in the
Master’s program as well as established scientific methods and knowledge,
within a pre-set time frame
- Compilation of a thesis in compliance with the established principles of scientific
writing
- Presentation of scientific results in comprehensible form to an audience of peers,
including scientific discussion
- Gaining key competencies regarding project management, presentation skills
and rhetoric skills
Content:
dependent on topic
Literature:
dependent on topic
Basis for:
–
Modes of learning
and teaching:
Master’s Thesis Selection of a suitable topic at one of the institutes of the
faculty, or exceptionally outside of the faculty (requires permission of the Examination Committee); research of scientific literature, design work and/or experimental work dependent on topic; consultation with the guiding assistants
and the primary supervisor ()
Estimation of
effort:
Active Time: 10 h
Preparation and Evaluation: 890 h
Sum: 900 h
Course assessment
and exams:
Written thesis and final presentation
Requirements
(formal):
At least the modules of the foundation and core subjects have to be completed
successfully.
142
Grading:
Grading of results obtained, written thesis, and final presentations by two reviewers in accordance with rules and regulations
Basierend auf Rev. 1575. Letzte Änderung am 08.10.2015 um 07:54 durch mreichert.
143