Pattern Recognition and Machine Learning

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Springer, Aug 17, 2006 - Computers - 738 pages
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. 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.

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Very nice book! I have been teaching this book for a couple of semesters in a graduate level course. I am finding all chapters very consistent in their approach to form up the whole field. It covers modern topics such as RVMs found hardly in classical texts. The book goes beyond such traditional coverages in the final chapters and exposes the reader into more advanced topics such as variational pattern recognition techniques. It is also nicely supported by helpful sample data sets with solutions to selected problems at each chapter.  

Review: Pattern Recognition and Machine Learning

User Review  - Oldrich - Goodreads

1. The book is mainly about Bayesian approach. And many important techniques are missing. This is the biggest problem I think. 2. “Inconsistent difficulty”, too much time spent on simple things and ... Read full review

About the author (2006)

Christopher M. Bishop is Assistant Director at Microsoft Research Cambridge, and also holds a Chair in Computer Science at the University of Edinburgh.

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