Contents:
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