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Machine Learning: The Art and Science of Algorithms that Make Sense of Data

Machine Learning: The Art and Science of Algorithms that Make Sense of Data

Machine Learning: The Art and Science of Algorithms that Make Sense of Data Hardback - 2012

by Peter Flach

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Hardback. New. Machine Learning brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, the book explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.
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Details

  • Title Machine Learning: The Art and Science of Algorithms that Make Sense of Data
  • Author Peter Flach
  • Binding Hardback
  • Condition New
  • Pages 410
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2012-09-20
  • Features Bibliography, Index
  • Bookseller's Inventory # B9781107096394
  • ISBN 9781107096394 / 1107096391
  • Weight 2.3 lbs (1.04 kg)
  • Dimensions 9.8 x 7.3 x 1 in (24.89 x 18.54 x 2.54 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Computers - General Information
  • Library of Congress subjects Machine learning, Apprentissage automatique - Manuels scolaires
  • Library of Congress Catalogue Number 2012289353
  • Dewey Decimal Code 004.67
  • Quantity available 10

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From the publisher

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

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