Pattern Recognition and Machine Learning (Information Science and Statistics)

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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Recent Reviews

5/5: From: Amazon; Posted: Oct-03-2009; one of the best books in the field; this book is one of the best books in the field--machine learning. this is the textbook that is used in most of the renowned university, and it qualifies for it! the contents are abundant, and full explanation and exercises will lead you...; read full review | report as inappropriate

5/5: From: Amazon; Posted: Jun-30-2009; Good seller.; Condition matched with the description. Responded quickly to an email regarding the shipping dates. Would have liked different options for shipping but would definitely buy again.; read full review | report as inappropriate

5/5: From: Amazon; Posted: Mar-19-2009; Nice item, fast shipping; The condition of the item is as descriped on the website. Ths shipping was also very fast. Highly recommended.; read full review | report as inappropriate

5/5: From: Amazon; Posted: Jan-15-2009; Faster than expected, and excellent product quality.; I was thrilled with both the speed with which my book was delivered (inspite of being overseas), and the excellent condition of the received book. I thoroughly recomment this seller.; read full review | report as inappropriate

Selected Reviews

5/5: From: Amazon; Posted: Oct-03-2009; one of the best books in the field; this book is one of the best books in the field--machine learning. this is the textbook that is used in most of the renowned university, and it qualifies for it! the contents are abundant, and full explanation and exercises will lead you...; read full review | report as inappropriate

3/5: From: Amazon; Posted: Jul-09-2008; concentrates too much on the easy stuff; The book is worth a look, but after some of 5 star reviews i read here, it was quite a disappointment. Yes, the book covers a lot of ground. Yes, the book has lots of nice pictures and easy examples, but that is exactly the problem....; read full review | report as inappropriate

2/5: From: Amazon; Posted: Sep-13-2008; Little emphasis on concepts; After reading "Pattern Recognition using Neural Networks" written by the same author, I was expecting a book of the same league: strong emphasis on the conceptual foundations, and a distillation of the great ideas of a field that is...; read full review | report as inappropriate