Pattern Recognition and Machine Learning (Information Science and Statistics)
Bishop, Christopher M.
ISBN 10: 0387310738 ISBN 13: 9780387310732
Published by Springer (edition ), 2006
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About this Item
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Synopsis:
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.
About the Author: Chris Bishop is a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, and in 2007 he was elected Fellow of the Royal Society of Edinburgh.
Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. He then joined Culham Laboratory where he worked on the theory of magnetically confined plasmas as part of the European controlled fusion programme.
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Bibliographic Details
Title: Pattern Recognition and Machine Learning (...
Publisher: Springer (edition )
Publication Date: 2006
Binding: Hardcover
Condition: Very Good
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Information Science and Statistics: Pattern Recognition and Machine Learning
Published by
Springer-Verlag New York Inc., 2006
ISBN 10: 0387310738
ISBN 13: 9780387310732
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