Bishop PRML review JEI

Bishop PRML review JEI - B O O K R E V I E W Pattern...

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Unformatted text preview: B O O K R E V I E W Pattern Recognition and Machine Learning Christopher M. Bishop, 73 pp., ISBN 0- 387-31073-8, Springer, New York s 2006 d , $74.95 hardcover. Reviewed by Nasser M. Nasrabadi, U.S. Army Research Laboratory, Adelphi, Maryland This book provides an introduction to the field of pattern recognition and machine learning. It gives an overview of several basic and advanced topics in machine learning theory. The book is definitely valuable to scientists and engineers who are involved in developing machine learn- ing tools applied to signal and image pro- cessing applications. This book is also suitable for courses on machine learning and pattern recognition, designed for ad- vanced undergraduates or PhD students. No previous knowledge of machine learn- ing concepts or algorithms is assumed, but readers need some knowledge of calculus and linear algebra. The book is comple- mented by a great deal of additional sup- ports for instructors and students. The sup- ports include solutions to the exercises in each chapter, the example data sets used throughout the book and the forthcoming companion book that deals with practical and software implementations of the key algorithms. A strong point of this book is that the mathematical expressions or algo- rithms are usually accompanied with col- orful graphs and figures. This definitely helps to communicate the concepts much better to the students or the interested re- searchers than pure description of the algo- rithms. The book also provides an interest- ing short biography of the key scientists and mathematicians who have contributed historically to the basic mathematical con- cepts and methods in each chapter. This book consists of 14 chapters cov- ering the basic concepts of the probability theory, classical linear regression, binary discrimination or classification, neural net- works, and advanced topics such as kernel methods, Bayesian graphical models, variational inference, Monte Carlo sam- pling methods, hidden Markov models, and fusion of classifiers. Chapter 1 intro- duces the basics of machine learning and classical pattern recognition by introducing...
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This note was uploaded on 11/03/2009 for the course CS 195f taught by Professor Johnson during the Spring '09 term at Sanford-Brown College.

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Bishop PRML review JEI - B O O K R E V I E W Pattern...

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