Understanding machine learning:

Referencias: páginas 385-393

Fundations -- A gentle start -- A formal learning model -- Learning via uniform convergence -- The bias-complexity trade -off -- The vc-dimension -- Nonuniform learnability -- The runtime of learning -- From therory 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 -- Additional learning models -- Online learning -- Clustering -- Dimensionality reduciton -- Generative models -- Feature selection and generatio -- Advanced theory -- Redamacher complexities -- Covering numbers -- Proof of the fundamental theorem of learning theory -- Multiclass learnability -- Compression bounds -- PAC-bayes