Understanding Machine Learning: From Theory to Algorithms

Shalev-shwartz, Shai/ Ben-David, Shai

ISBN 10: 1107057132 / ISBN 13: 9781107057135

Edité par Cambridge Univ Pr, 2014

A propos de cet article

397 pages. 10.00x7.00x1.00 inches. In Stock.

N° de réf. du vendeur x-1107057132

Détails bibliographiques

• Titre: Understanding Machine Learning: From Theory to Algorithms
• Éditeur: Cambridge Univ Pr
• Date de parution: 2014
• Langue: anglais
• ISBN à 10 chiffres: 1107057132
• ISBN à 13 chiffres: 9781107057135
• Reliure: Hardcover
• État de l'article: Brand New
• Poids: 0,91 kilogramme

A propos de ce titre

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.

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.