New text book ... Pattern Recognition and Machine Learning Christopher M. Bishop Springer (2006).

This completely new textbook provides 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. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below). Extensive support is provided for course instructors.

Latest news

• Solutions to non-www exercises are available to bona fide course tutors from the publisher. To register, go here and select "Instructor's solution manual".

• The book has been translated into Japanese. Volume 1 (Chapters 1-5 plus the appendices) is available here, and volume 2 (Chapters 6-14) will be available in the Spring. Support for the Japanese edition is available from here.

• Amazon.com is currently offering the book at $58.80 (22% discount compared with the regular price of $74.95) with free shipping.

Downloads

• Slides for Chapter 1 (Introduction) in PDF, PowerPoint, and PowerPoint 2007 formats.

• Slides for Chapter 8 (Graphical Models) in PDF, PowerPoint, and PowerPoint 2007 formats.

• A complete solutions manual for the www exercises in PDF format (version: 5 October, 2007)

• Data sets

• Complete set of Figures in JPEG, PDF and EPS formats

• A PDF file of errata. There are two versions of this. To determine which one to download, look at the bottom of the page opposite the dedication photograph in your copy of the book. If it says "corrected printing 2007" then download version 2. Otherwise download version 1.

• Contents list and sample chapter (Chapter 8: Graphical Models) in PDF format

• Longer synopsis

• A review of the book by John Maindonald from ANU, published in Journal of Statistical Software

Mailing list

If you would like to be kept informed of developments associated with this book, such as the release of the solutions manual, or the software, then please subscribe to the PRML mailing list by sending a blank email to

join-prml [at] list.research.microsoft.com

(Note that web enrolment is no longer available.)

Pattern Recognition and Machine Learning is available from:

• Springer

• Amazon USA

• Amazon UK

• Amazon France

Coming soon

• Worked solutions to non-WWW exercises (for course tutors) available from Springer for chapters 8-14.

• Lecture slides to accompany each chapter

• Matlab software (Netlab version 2, written by Ian T. Nabney) implementing most of the major algorithms discussed in the book

Companion textbook update

Originally it had be intended to write a “companion” textbook to PRML in collaboration with Ian Nabney, accompanied by Matlab software (version 3 of Netlab) implementing many of the algorithms discussed in PRML. The companion book was to have served two main purposes: (i) to explain some useful algorithms, mainly concerned with the solution of optimization problems which arise in machine learning, for which there was insufficient space in the main text book, and (ii) to provide an overview and manual for the Netlab v3 software along with guidance on the practical application of machine learning. I am pleased to say that we have now decided instead to make most of this material, including the software, freely available from the web. In particular the optimization algorithms will be discussed in a tutorial paper which will be available as a PDF file from the PRML book web site. The references to Bishop and Nabney (2008) in the book now refer to this tutorial paper. The Netlab v3 software, along with supporting documentation, demonstrations and tutorials, will be available directly from the Netlab web site.