000			01832nam a2200241Ia 4500
001			38345
003			IN-BdCUP
005			20240510151644.0
008			230413s2023 000 0 eng
020			_a1107512824
040			_beng _cIN-BdCUP
041			_aeng
082			_a006.3 _bSHA
100			_aShalev-Shwartz, Shai
245		0	_aUnderstanding machine learning : _bfrom theory to algorithms / _cShai Shalev-Shwartz and Shai Ben-David
260			_aNew Delhi : _bCambridge University Press, _c2014.
300			_a397 p. ; _c22 cm.
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			_aAlgorithms
650			_aUnderstanding Machine Learning
700			_aBen-David, Shai
942			_2ddc _cBK
999			_c29413 _d29413