Pattern Recognition and Machine Learning

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Springer, Aug 17, 2006 - Computers - 738 pages
34 Reviews
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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Review: Pattern Recognition and Machine Learning

User Review  - Хотло Ширнууд - Goodreads

Excellent book with very detailed explanations on the fundamentals of the statistics and machine learning. Read full review

Review: Pattern Recognition and Machine Learning

User Review  - John - Goodreads

Dense but useful. Wish I hadn't sold my copy. 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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