01873cam a2200217 i 45000010009000000050017000090080041000260200029000670200026000960400013001220820022001351000025001572450106001822600050002883000045003385201158003836500022015416500016015636500055015797000021016341805364820170107110154.0140304s2014    nyua     b    001 0 eng    a9781107057135 (hardback)  a1107057132 (hardback)  cPK-LaUMT00a006.31223bSHA-U1 aShalev-Shwartz, Shai10aUnderstanding machine learning :bfrom theory to algorithms /cShai Shalev-Shwartz and Shai Ben-David  aNew York :bCambridge University Press,c2014  axvi, 397 pages :billustrations ;c26 cm  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"-- 0aMachine learning. 0aAlgorithms. 7aCOMPUTERS / Computer Vision & Pattern Recognition.1 aBen-David, Shai.