000			01594 a2200193 4500
999			_c198794 _d198794
020			_a9781107512825
040			_cCUS
100			_a Shalev-Shwartz, Shai _914111
245			_aUnderstanding Machine Learning: From Theory to Algorithms/ _cShai Shalev-Shwartz, Shai Ben-David
250			_a1st ed.
260			_bCambridge University Press, _aNew Delhi: _c2014
300			_axvi, 397 p. : _bill. ; _c26 cm.
505			_aIntroduction -- Part I. Foundations -- 2 A gentle start -- 3 A formal learning model -- 4 Learning via uniform convergence -- 5 The bias-complexity tradeoff -- 6 The VC-dimension -- 7 Nonuniform learnability -- 8 The runtime of learning -- Part II. From Theory to Algorithms -- 9 Linear predictors -- 10 Boosting -- 11 Model selection and validation -- 12 Convex learning problems -- 13 Regularization and stability -- 14 Stochastic gradient descent -- 15 Support vector machines -- 16 Kernel methods -- 17 Multiclass, ranking, and complex prediction problems -- 18 Decision trees -- 19 Nearest neighbor -- 20 Neural networks -- Part III. Additional Learning Models -- 21 Online learning -- 22 Clustering -- 23Dimensionality reduction -- 24 Generative models -- 25 Feature selection and generation -- Part IV. Advanced Theory -- 26 Rademacher complexities -- 27 Covering numbers -- 28 Proof of the fundamental theorem of learning theory -- 29 Multiclass learnability -- 30 Compression bounds -- 31 PAC-Bayes.
650			_aMachine Learning _911899
650			_aAlgorithms _910139
650			_aComputers _98557
700			_aBen-David, Shai _914112
942			_cWB16 _03