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Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning
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Bayesian Reasoning and Machine Learning Hardback - 2012

by Barber, David

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Cambridge University Press, 2012-03-12. 1. hardcover. New. 7.75x1.50x10.00. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Bayesian Reasoning and Machine Learning
  • Author Barber, David
  • Binding Hardback
  • Edition 1
  • Condition New
  • Pages 735
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2012-03-12
  • Features Bibliography, Index, Table of Contents, Textbook
  • Bookseller's Inventory # DADAX0521518148
  • ISBN 9780521518147 / 0521518148
  • Weight 3.8 lbs (1.72 kg)
  • Dimensions 9.8 x 7.7 x 1.5 in (24.89 x 19.56 x 3.81 cm)
  • Size 7.75x1.50x10.00
  • Category Computers - General Information
  • Library of Congress subjects Bayesian statistical decision theory, Machine learning
  • Library of Congress Catalogue Number 2011035553
  • Dewey Decimal Code 006.31
  • Quantity available 6

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Reader reviews for Bayesian Reasoning and Machine Learning

From the publisher

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
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