Understanding Machine Learning - From Theory to Algorithms (Hardcover)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	May 2014
First published:	May 2014
Authors:	Shai Shalev-Shwartz • Shai Ben-David
Dimensions:	260 x 183 x 28mm (L x W x T)
Format:	Hardcover
Pages:	410
ISBN-13:	978-1-107-05713-5
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-107-05713-2
Barcode:	9781107057135

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