Pattern Recognition and Machine Learning (Paperback, Softcover reprint of the original 1st ed. 2006)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Information Science and Statistics
Release date:	August 2016
First published:	2006
Authors:	Christopher M. Bishop
Dimensions:	254 x 178 x 44mm (L x W x T)
Format:	Paperback
Pages:	738
Edition:	Softcover reprint of the original 1st ed. 2006
ISBN-13:	978-1-4939-3843-8
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Computing & IT > Applications of computing > Image processing > General
LSN:	1-4939-3843-6
Barcode:	9781493938438

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