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Aim:This course covers the basics of AI and its applications, Searching methods and presentation
methods will be studied, The expert systems will be discussed , neural network will be
covered.
Learning outcomes:
By the end of this course the student will be able to
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Know the AI and its applications.
Practice the searching methods in AI.
Explore the knowledge base in AI.
Discover the expert systems & Neural networks.
Syllabus Outline:
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Define AI.
Fundamental issues in AI.
AI applications.
Searching and constraint satisfaction.
Knowledge representation and reasoning.
Neural networks, Introduction, history, Biological neural Networks, Artificial neural
networks, information processing using Artificial networks, some applications.
Teaching Methods:
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Lectures and tutorials
Assessment Methods
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Tests, home works and Reports.
Final Examination.
References:
Lecture number
Lecture time
Stuart Russell and Peter Norvig, Artificial
Intelligence: A Modern Approach, 2nd ed., 2008
Topics
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2
Define AI.
2
2
Fundamental issues in AI.
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2
AI applications.
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5
2
2
Searching and constraint satisfaction
Test (1)
7
8
9
10
11
2
2
2
2
2
Neural networks, Introduction, history, Biological
neural Networks
Artificial neural networks
Test (2)
13
2
14
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Some applications
(