Understanding machine learning : from theory to algorithms / Shai, Shalev-Shwartz / Shai, Ben-David [ Livre]

Auteur principal: Shalev-Shwartz, ShaiCo-auteur: Ben-David, ShaiLangue: Anglais ; de l'oeuvre originale, Anglais.Publication : New York, NY : Cambridge University Press, 2014Description : 1 vol. (XVI-397 p.) ; 26 cmISBN: 9781107057135.Classification: I Intelligence artificielle, Machine Learning et Data ScienceRésumé: La 4e de couverture indique : "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.".Sujet - Nom commun: Apprentissage automatique | Algorithmes | Machine learning
Current location Call number Status Notes Date due Barcode
ENS Rennes - Bibliothèque
Informatique
I SHA (Browse shelf) Available I Intelligence artificielle, Machine Learning et Data Science 041160

La 4e de couverture indique : "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."

Powered by Koha