Understanding machine learning. From theory to algorithms. (English) Zbl 1305.68005

Shalev-Shwartz, Shai; Ben-David, Shai

Cambridge: Cambridge University Press (ISBN 978-1-107-05713-5/hbk; 978-1-107-29801-9/ebook). xvi, 397 p. (2014).

Publisher’s description: 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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

MSC:

68-01	Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science
68T05	Learning and adaptive systems in artificial intelligence