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020			_a9781107298019
040			_aIITJ
082			_a006.31 _bSh93U
100			_aShalev-Shwartz, Shai _eAuthor _lEnglish _91866
245		0	_aUnderstanding Machine Learning _b: From Theory to Algorithms _c/ by Shai Shalev-Shwartz and Shai Ben-David. _h[Electronic Resource]
260			_aCambridge _b: Cambridge University Press, _c2014
300			_axvi, 397p.
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 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.
650			_aComputer Science _9926
650			_aMachine Learning _915726
650			_aPattern Recognition _915727
658			_aMachine Learning I
658			_cCSL7XX0
700			_aBen-David, Shai. _i[Author] _91868
856			_uhttps://doi.org/10.1017/CBO9781107298019 _qPDF _yClick to Access the Online Book
942			_cEBK _2ddc
999			_c12246 _d12246