Understanding machine learning : from theory to algorithms Shai Shalev-Shwartz, the Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada

Introduction -- I. Foundations -- A gentle start -- A formal learning model -- Learning via uniform convergence -- The bias-complexity tradeoff -- The VC-dimension -- Nonuniform learnability -- The runtime of learning -- II. From Theory to Algorithms -- Linear predictors -- Boosting -- Model selection and validation -- Convex learning problems -- Regularization and stability -- Stochastic gradient descent -- Support vector machines -- Kernel methods -- Multiclass, ranking, and complex prediction problems -- Decision trees -- Nearest neighbor -- Neural networks -- III. Additional Learning Models -- Online learning -- Clustering -- Dimensionality reduction -- Generative models -- Feature selection and generation -- IV. Advanced Theory -- Rademacher complexities -- Covering numbers -- Proof of the fundamental theorem of learning theory -- Multiclass learnability -- Compression bounds -- PAC-Bayes