000			01921cam a2200241 i 4500
001			18053648
005			20170107110154.0
008			140304s2014 nyua b 001 0 eng
020			_a9781107057135 (hardback)
020			_a1107057132 (hardback)
040			_cPK-LaUMT
082	0	0	_a006.31 _223 _bSHA-U
100	1		_aShalev-Shwartz, Shai
245	1	0	_aUnderstanding machine learning : _bfrom theory to algorithms / _cShai Shalev-Shwartz and Shai Ben-David
260			_aNew York : _bCambridge University Press, _c2014
300			_axvi, 397 pages : _billustrations ; _c26 cm
520			_a"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"--
650		0	_aMachine learning.
650		0	_aAlgorithms.
650		7	_aCOMPUTERS / Computer Vision & Pattern Recognition.
700	1		_aBen-David, Shai.
942			_cBK
999			_c89907 _d89907