PATTERN RECOGNITION AND MACHINE LEARNING

By:

BISHOP CHRISTOPHER

Material type: Text

TextPublication details: US SPRINGER LINK 2006Description: xx, 738ISBN:

978-0-387-31073-2

DDC classification:

006.3/BIS

Summary: This book 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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Holdings
Item type	Current library	Collection	Call number	Status	Notes	Date due	Barcode
Books	Symbiosis Institute of Computer Studies and Research Reference	Reference	006.3/BIS (Browse shelf(Opens below))	Available	PATTERN PERCEPTION, MACHINE LEARNING		SICSR-B-19426

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This book 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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