Understanding Machine Learning: From Theory to Algorithms- - Couverture rigide

Shalev-Shwartz, Shai

Couverture rigide

ISBN 10 : 1107057132 ISBN 13 : 9781107057135

Editeur : Cambridge University Press, 2014

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 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.

A propos du livre

À propos des auteurs

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.

Détails de l'édition

• Éditeur: Cambridge University Press
• Date d'édition: 2014
• ISBN 10: 1107057132
• ISBN 13: 9781107057135
• Reliure: Relié
• Langue: anglais
• Numéro d'édition: 1
• Nombre de pages: 414
• Coordonnées du fabricant: non disponible