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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine
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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) Hardback - 2012

by Murphy, Kevin P

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The MIT Press. Illustrated. Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Details

  • Title Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
  • Author Murphy, Kevin P
  • Binding Hardback
  • Edition Illustrated
  • Condition Used - Very good
  • Pages 1104
  • Volumes 1
  • Language ENG
  • Publisher The MIT Press, U.S.A.
  • Publication date 2012-08
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # 0262018020-11-1
  • ISBN 9780262018029 / 0262018020
  • Weight 4.3 lbs (1.95 kg)
  • Dimensions 9.1 x 8.2 x 1.7 in (23.11 x 20.83 x 4.32 cm)
  • Age range 18 to UP years
  • Grade levels 13 - UP
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Computers - General Information
  • Library of Congress subjects Probabilities, Machine learning
  • Library of Congress Catalogue Number 2012004558
  • Dewey Decimal Code 006.31
  • Quantity available 1

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From the publisher

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

About the author

Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.

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