Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

Description

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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Details

Author(s)
Christopher M. Bishop
Format
Hardback | 738 pages
Dimensions
178 x 254 x 43.18mm | 2,147g
Publication date
06 Apr 2011
Publisher
Springer-Verlag New York Inc.
Publication City/Country
New York, NY, United States
Language
English
Edition Statement
1st ed. 2006. Corr. 2nd printing 2011
Illustrations note
XX, 738 p.
ISBN10
0387310738
ISBN13
9780387310732
Bestsellers rank
79,611