Understanding machine learning: From theory to algorithms

Shai Shalev-Shwartz, Shai Ben-David

Research output: Book/ReportBookpeer-review

2720 Scopus citations

Abstract

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.

Original languageAmerican English
PublisherCambridge University Press
Number of pages397
Volume9781107057135
ISBN (Electronic)9781107298019
ISBN (Print)9781107057135
DOIs
StatePublished - 1 Jan 2013

Bibliographical note

Publisher Copyright:
© Shai Shalev-Shwartz and Shai Ben-David 2014.

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