Understanding Machine Learning

Shai Shalev-Shwartz (Author), Shai Ben-David (Author)

Hardcover: 410 pages

Publisher: Cambridge University Press (May 19 2014)

Language: English

ISBN-10: 1107057132

ISBN-13: 978-1107057135

Product Dimensions: 18.3 x 2.8 x 26 cm

Shipping Weight: 907 g

页面参数仅供参考，具体以实物为准

内容简介

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.