"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"--
%0 Book
%1 shalevshwartz2014understanding
%A Shalev-Shwartz, Shai
%A Ben-David, Shai
%D 2014
%K ML
%T Understanding machine learning : from theory to algorithms
%U http://www.worldcat.org/search?qt=worldcat_org_all&q=9781107057135
%X "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"--
%@ 9781107057135 1107057132
@book{shalevshwartz2014understanding,
abstract = {"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"--},
added-at = {2016-11-03T05:09:52.000+0100},
author = {Shalev-Shwartz, Shai and Ben-David, Shai},
biburl = {https://www.bibsonomy.org/bibtex/28c50d1514809b0c7c58c6da9efba9355/strauman},
interhash = {125d708c7b440a3cfeb6146e83ab5de3},
intrahash = {8c50d1514809b0c7c58c6da9efba9355},
isbn = {9781107057135 1107057132},
keywords = {ML},
refid = {866619766},
timestamp = {2016-11-03T05:09:52.000+0100},
title = {Understanding machine learning : from theory to algorithms},
url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=9781107057135},
year = 2014
}