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Pattern Recognition) Book
Book Synopsis
A Proven, Hands-On
Approach for Students
without a Strong Statistical
Foundation Since the bestselling first edition was
published, there have been
several prominent
developments in the field of
machine learning, including
the increasing work on the
statistical interpretations of
machine learning algorithms.
Unfortunately, computer
science students without a
strong statistical background
often find it hard to get
started in this area.
Remedying this deficiency,
Machine Learning: An
Algorithmic Perspective,
Second Edition helps
students understand the
algorithms of machine
learning. It puts them on a
path toward mastering the
relevant mathematics and
statistics as well as the
necessary programming and
experimentation. New to the
Second Edition Two new
chapters on deep belief
networks and Gaussian
processes Reorganization of
the chapters to make a more
natural flow of content
Revision of the support
vector machine material,
including a simple
implementation for
experiments New material on
random forests, the
perceptron convergence
theorem, accuracy methods,
and conjugate gradient
optimization for the multilayer perceptron Additional
discussions of the Kalman
and particle filters Improved
code, including better use of
naming conventions in
Python Suitable for both an
introductory one-semester
Book details
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Author : Stephen
Marsland
Pages : 457 pages
Publisher : Chapman
and Hall/CRC 2014-1008
Language : English
ISBN-10 : 1466583282
ISBN-13 :
9781466583283
course and more advanced
courses, the text strongly
encourages students to
practice with the code. Each
chapter includes detailed
examples along with further
reading and problems. All of
the code used to create the
examples is available on the
author’s website.