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.

Details

Author(s)
Christopher M. Bishop
Format
Paperback | 738 pages
Dimensions
178 x 254 x 41.15mm | 1,921g
Publication date
23 Aug 2016
Publisher
Springer-Verlag New York Inc.
Publication City/Country
New York, United States
Language
English
Edition
Revised
Edition Statement
Softcover reprint of the original 1st ed. 2006
Illustrations note
XX, 738 p.
ISBN10
1493938436
ISBN13
9781493938438
Bestsellers rank
395,481