Understanding Machine Learning From Theory to Algorithms

Shalev-Shwartz, Shai

ISBN 10: 1107057132 / ISBN 13: 9781107057135

Edité par Cambridge University Press, 2014

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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.

Détails bibliographiques

Titre : Understanding Machine Learning From Theory ...
Éditeur : Cambridge University Press
Date d'édition : 2014
Reliure : Couverture rigide
Etat : Good