Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

This leading 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.

To view inside this book go to Amazon.

Available from


  • Contents list and sample chapter (Chapter 8: Graphical Models) in PDF format.
  • Solutions manual for the www exercises in PDF format (version: 8 September, 2009).
  • Complete set of Figures in JPEG, PNG, PDF and EPS formats.
  • A PDF file of errata. There are three 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 ...2009" then download Version 3. If it says "corrected ...2007" then download Version 2. Otherwise download Version 1.
  • The book has been translated into Japanese in two volumes. Volume 1 contains chapters 1-5 plus the appendices, while Volume 2 contains chapters 6-14. Support for the Japanese edition is available from here.
  • A third party Matlab implementation of many of the algorithms in the book. I’ve not tried this myself and cannot comment on the quality.

Support for course tutors