Great book- clear explanation of important topics

books:


	• Pattern Recognition and Machine Learning (Information Science and Statistics) Christopher M. Bishop Springer, 2007 - 738 pages average customer review:based on 38 reviews view larger image
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highly recommended

Authorative text

I am a PhD student who wanted to own a good book on pattern recognition. I asked my professor, who had recently attended an international conference on speech recognition, which book to buy. He said that several top academics in the field at the conference had agreed that this was THE book to have, and he agrees with them.

After reading though the first few chapters I am impressed by the structured way concepts are related. I like that the basic probability theory needed to understand the concepts are recapped and explained in an understandable way.

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Great book for Learning Machine Learning

This book is quite good in explaining basics of pattern recognition and machine learning and enables the reader to relate the theory to diverse practical applications. The explanations are very simple. It is better to have thorough knowledge of random vectors and linear algebra to derive maximum benefit from this book. I would recommend this book to any one new to this field.

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Great book- clear explanation of important topics

Provides a simple introduction to probability theory, but also contains some of the best explanations available on some advanced topics like variational approximations and relevance vector machines. The whole book is easy to read, with good examples. Note that if you are interested in model selection in the variational approximation section- you should download the errata- try searching for "Pattern Recognition and Machine Learning Errata".

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Awesome

Start right from the first page. No gimmicks. Plain old mathematics and useful stuff, then to machine learning. You always know, the rationale behind the chapters or the sentence. Very inspiring.

A brilliant book

This book gives a comprehensive understanding of machine leraning. The way the author puts forth a myriad of topics is appreciable. The book takes more of an algorithmic standpoint rather than a statistical standpoint on Machine Learning, and is highly recommended for anyone starting in this field.

reviews: page 1, 2, 3, 4, 5, 6, 7, 8

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

Coming soon:

*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)

*For instructors, worked solutions to remaining exercises from the Springer web site

*Lecture slides to accompany each chapter

*Data sets available for download

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