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Understanding Machine Learning: From Theory to Algorithms 1st Edition

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Shai Shalev-Shwartz (Author)
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Shai Shalev-Shwartz (Author), Shai Ben-David (Author)
4.1 out of 5 stars 9 customer reviews
ISBN-13: 978-1107057135
ISBN-10: 1107057132
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Product Details

  • Hardcover: 409 pages
  • Publisher: Cambridge University Press; 1 edition (May 19, 2014)
  • Language: English
  • ISBN-10: 1107057132
  • ISBN-13: 978-1107057135
  • Product Dimensions: 7 x 1.1 x 10 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (9 customer reviews)
  • Amazon Best Sellers Rank: #83,436 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

5.0 out of 5 starsExcellent book based on statistical learning theory
By Yung-Hsuan Chao on August 5, 2014
Format: Hardcover Verified Purchase
This book provides a great story line along with solid proofs of machine learning theories and algorithms.
Each chapter is rather short (15-20 pages), yet is well written to convey the topic in detail, making the book comfortable to read.
Moreover, the connection among consecutive chapters is strong, giving an excellent coarse-to-fine introduction on sophisticated theories.

Over the past few years, I have read several machine learning books, and this is the one solidly based on "statistical learning theory".
Compared to other books that give only brief description to this aspect, this book does a good job not only on providing the basic proofs, but also on extending the theories to well-known practical algorithms, supporting the success of these algorithms and showing how theories can be used to design or analyze practical algorithms. For whom eager to know more about learning theory, this is a must-read book.
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4.0 out of 5 starsenjoying the book...
By Eve Freeman on June 17, 2014
Format: Kindle Edition Verified Purchase
First, let me just say I regret purchasing the kindle version, as it is difficult to read the math symbols on the kindle, and even somewhat difficult to read them on the kindle for mac app on a big screen. Zoomed in leaves the symbols the same size (it appears as though they're images), with the surrounding text large. Perhaps this is a problem on most math texts, but I was disappointed.

I'm enjoying the book. It reads like a textbook that one might find at a university, and has exercises and notes for the order you'd go through it while teaching a class. I find it well-written and for the most part, easy to digest--a bit heavy on the math for what I was looking for, but you can skim over it for the ideas.
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5.0 out of 5 starsMust for understanding of the theory of ML (if you understand what has to be understood ... )
By Penseur on November 21, 2014
Format: Hardcover
This is an excellent introduction to the theory of Machine Learning (ML). I would like though to stress the word "theory".
This is probably not the first introductory book in ML (the readers with strong mathematical background can disregard this reservation, they indeed can use this as !), for the beginners who want to learn the basic concepts of the ML and to understand the motivation behind the mathematical concepts I would recommend something like "Learning from Data" by Yaser Abu-Mostafa et al. http://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/1600490069/ref=sr_1_1?s=books&ie=UTF8&qid=1416599862&sr=1-1&keywords=learning+from+data complemented by the e-chapters in the online forum http://book.caltech.edu/bookforum/ and, probably, by online course (see http://edx.org ).
But for those who have already got some basic ideas about the concepts of ML and the motivation for the theoretical justification of the algorithms, this is definitely should be the next book to read: it provides the rigorous proofs and presents the concepts and algorithms in clear mathematical language. There is no need to be scared though: the presentation of the stuff is excellent, the chapters are short enough in order to enable the reader to advance in reasonable steps (the book is derived from the lectures presented by both of the authors), there are excellent exercises. The theory is indeed well connected to practical algorithms and real applications as promised by the subtitle :)
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5.0 out of 5 starsA Good Introduction to the Mathematical Foundations of Many Popular Machine Learning Algorithms
By Rmg on December 27, 2014
Format: Kindle Edition Verified Purchase
This is an excellent introduction to machine learning which fills an important gap in the literature
by introducing students to formal broad conceptual frameworks for understanding, comparing, analyzing,
and designing large classes of popular machine learning algorithms. These frameworks are explicitly presented
as mathematical theorems but the authors are careful about explaining the underlying assumptions of key theorems and
interpreting the conclusions of such theorems. Richard M. Golden.
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5.0 out of 5 starsThis is a very well written book. The chapters ...
By Vineet Gupta on February 6, 2015
Format: Hardcover Verified Purchase
This is a very well written book. The chapters are short (bite-sized), but are very lucid. Almost all proofs are given in detail. I am enjoying reading it a lot.
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