Understanding Machine Learning: From Theory to Algorithms

Shalev-Shwartz, Shai

ISBN 10: 1107057132 / ISBN 13: 9781107057135

Published by Cambridge University Press, 2014

Item in good condition and has highlighting/writing on text. Used texts may not contain supplemental items such as CDs, info-trac etc. Seller Inventory # 00087735825

Synopsis:

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.

About the Authors: Shai Shalev-Shwartz is an Associate Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel.

Shai Ben-David is a Professor in the School of Computer Science at the University of Waterloo, Canada.

Bibliographic Details

Title: Understanding Machine Learning: From Theory ...
Publisher: Cambridge University Press
Publication Date: 2014
Binding: Hardcover
Condition: Good