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 Title Pattern Recognition and Machine Learning
 Author(s) Christopher M. Bishop
 Publisher: Springer (August 17, 2006); eBook (PDF by Microsoft)
 Permission: Link to PDF on the Author's Homepage at Microsoft
 Hardcover 738 pages
 eBook PDF (758 pages)
 Language: English
 ISBN10: 0387310738
 ISBN13: 9780387310732
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Book 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 selfcontained introduction to basic probability theory.
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 selfcontained introduction to basic probability theory.
About the Authors Christopher M. Bishop is the Laboratory Director at Microsoft Research Cambridge, Professor of Computer Science at the University of Edinburgh and a Fellow of Darwin College, Cambridge.
 Bayesian Thinking
 Machine Learning
 Deep Learning and Neural Networks
 Artificial Intelligence
 Data Analysis and Data Mining