Understanding Machine Learning: From Theory to Algorithms/
Shai Shalev-Shwartz, Shai Ben-David
- 1st ed.
- New Delhi: Cambridge University Press, 2014
- xvi, 397 p. : ill. ; 26 cm.
Introduction -- 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.