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Shai Shalev-Shwartz
Shai Ben-David
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Understanding Machine Learning: From Theory to Algorithms 1st Edition

by Shai Shalev-Shwartz (Author), Shai Ben-David (Author)
4.4 out of 5 stars 230 ratings
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  1. ISBN-10
    1107057132
  2. ISBN-13
    978-1107057135
  3. Edition
    1st
  4. Publisher
    Cambridge University Press
  5. Publication date
    May 19, 2014
  6. Language
    English
  7. Dimensions
    7 x 0.94 x 10 inches
  8. Print length
    410 pages
  9. See all details
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Frequently bought together

  • Understanding Machine Learning: From Theory to Algorithms
  • +
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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  • Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)
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Editorial Reviews

Review

"This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data."
Bernhard Schölkopf, Max Planck Institute for Intelligent Systems

"This is a timely text on the mathematical foundations of machine learning, providing a treatment that is both deep and broad, not only rigorous but also with intuition and insight. It presents a wide range of classic, fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great book for anyone interested in the mathematical and computational underpinnings of this important and fascinating field."
Avrim Blum, Carnegie Mellon University

"This text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area, it covers the range from theoretical foundations to algorithms, at a level appropriate for an advanced undergraduate course."
Peter L. Bartlett, University of California, Berkeley

Book Description

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Product details

  • Publisher ‏ : ‎ Cambridge University Press; 1st edition (May 19, 2014)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 410 pages
  • ISBN-10 ‏ : ‎ 1107057132
  • ISBN-13 ‏ : ‎ 978-1107057135
  • Item Weight ‏ : ‎ 2.01 pounds
  • Dimensions ‏ : ‎ 7 x 0.94 x 10 inches
  • Customer Reviews:
    4.4 out of 5 stars 230 ratings

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  1. Shai Shalev-Shwartz

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  2. Shai Ben-David

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Customer reviews

4.4 out of 5 stars
4.4 out of 5
230 global ratings
5 star
70%
4 star
13%
3 star
9%
2 star
4%
1 star
4%

Top reviews from the United States

Eve Freeman
4.0 out of 5 stars enjoying the book...
Reviewed in the United States 🇺🇸 on June 18, 2014
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23 people found this helpful
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C. Tomasi
5.0 out of 5 stars This is hands down the best. Rather than a laundry list of techniques
Reviewed in the United States 🇺🇸 on March 14, 2018
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30 people found this helpful
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Krishna
5.0 out of 5 stars South Asia Edition to USA customer
Reviewed in the United States 🇺🇸 on March 2, 2021
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Krishna
5.0 out of 5 stars South Asia Edition to USA customer
Reviewed in the United States 🇺🇸 on March 2, 2021
Paperback book sell only at South Asia edition and shipped to California, USA. Zoom the picture to see edition details on bottom right corner.

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Amazon_customer
3.0 out of 5 stars Very Nice book but the paperback international version is awful!
Reviewed in the United States 🇺🇸 on February 24, 2019
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Ruby Shamir
5.0 out of 5 stars Great book !
Reviewed in the United States 🇺🇸 on May 18, 2017
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12 people found this helpful
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Aardvark8
3.0 out of 5 stars Mixed
Reviewed in the United States 🇺🇸 on February 18, 2019
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2 people found this helpful
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Ittai Artzi
5.0 out of 5 stars covers every subject that I come over in articles and want to understand better, good exercises
Reviewed in the United States 🇺🇸 on January 25, 2018
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4 people found this helpful
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Rmg
5.0 out of 5 stars A Good Introduction to the Mathematical Foundations of Many Popular Machine Learning Algorithms
Reviewed in the United States 🇺🇸 on December 28, 2014
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14 people found this helpful
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Top reviews from other countries

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Andrew
5.0 out of 5 stars it is the best Machine Learning textbook at upper undergraduate/graduate level
Reviewed in the United Kingdom 🇬🇧 on June 11, 2017
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Steven Hutt
5.0 out of 5 stars a different kind of ML book
Reviewed in the United Kingdom 🇬🇧 on April 18, 2016
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One person found this helpful
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Katastrofa
1.0 out of 5 stars Very poor editing quality
Reviewed in the United Kingdom 🇬🇧 on May 27, 2018
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Ishraq u
3.0 out of 5 stars Colour
Reviewed in the United Kingdom 🇬🇧 on September 5, 2019
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prof
1.0 out of 5 stars Non corrispondente
Reviewed in Italy 🇮🇹 on April 5, 2018
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6 people found this helpful
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