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Understanding Machine Learning: From Theory to Algorithms
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Understanding Machine Learning: From Theory to Algorithms Hardcover - 2014

by Shai Shalev-Shwartz; Shai Ben-David


From the publisher

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Details

  • Title Understanding Machine Learning: From Theory to Algorithms
  • Author Shai Shalev-Shwartz; Shai Ben-David
  • Binding Hardcover
  • Edition International Ed
  • Pages 410
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Date 2014-05-19
  • ISBN 9781107057135 / 1107057132
  • Weight 1.95 lbs (0.88 kg)
  • Dimensions 10.1 x 6.9 x 1.1 in (25.65 x 17.53 x 2.79 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Library of Congress subjects Algorithms, Machine learning
  • Library of Congress Catalog Number 2014001779
  • Dewey Decimal Code 006.31
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Understanding Machine Learning: From Theory to Algorithms

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Understanding Machine Learning: From Theory to Algorithms
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by Shalev-Shwartz, Shai; Ben-David, Shai

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