000			nam a22 7a 4500
999			_c29486 _d29486
008			190427b xxu||||| |||| 00| 0 eng d
020			_a9781107512825 _c(pbk)
082			_a006.31 _bSHA
100			_aShalev-Shwartz, Shai
245			_aUnderstanding machine learning : from theory to algorithms
260			_aNew Delhi : _bCambridge University Press, _c2014
300			_axvi, 397 p. : _bill. ; _c23.2 cm.
365			_aINR _b995.00 _d00
504			_aIncludes bibliographical references and index.
520			_aMachine 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
650			_aComputer vision &​ pattern recognition
650			_aMachine learning
650			_aAlgorithms
942			_2ddc _cBK