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Pattern Recognition and Machine Learning Christopher M. Bishop

By: Material type: TextTextLanguage: English Publication details: Springer 2006 New YorkEdition: 1st EdDescription: 738p. 17.78 x 3.84 x 25.4 cmISBN:
  • 9781493938438
Subject(s): DDC classification:
  • 006.42 BIS
Summary: 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.
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Item type Current library Call number Materials specified Status Date due Barcode
Books Books Rashtriya Raksha University 006.4 BIS (Browse shelf(Opens below)) Available 11508
Books Books Rashtriya Raksha University 6.42 BIS (Browse shelf(Opens below)) Available 8122
Books Books Rashtriya Raksha University 6.42 BIS (Browse shelf(Opens below)) Available 8123

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.

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