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Understanding Machine Learning: From Theory to Algorithms
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  • Title: Understanding Machine Learning: From Theory to Algorithms
  • Author(s): Shai Shalev-Shwartz and Shai Ben-David
  • Publisher: CAMBRIDGE INDIA; 1st edition (January 1, 2015); eBook (Free Online Copy)
  • Permission: "This copy is for personal use only. Not for distribution. Do not post."
  • Hardcover/Paperback: 410 pages
  • eBook: PDF (449 pages)
  • Language: English
  • ISBN-10: 1107057132
  • ISBN-13: 978-1107057135
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Book Description

Machine learning makes use of computer programs to discover meaningful patters in complex data. It is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage.

The authors explain the "hows" and "whys" of the most important machine-learning algorithms, as well as their inherent strengths and weaknesses, making the field accessible to students and practitioners in computer science, statistics, and engineering.

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.

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