Topic 
Notes 
Additional Readings 
Introduction, Sample complexity bound for learning axis parallel rectangles. 
Chapter 1 of Kearnes Vazirani book (COLT book) 

Definition of PAC Learning 
Chapter 12 of Shai ShalevShwartz and Shai BenDavid book (UML book) 
A Theory of the Learnable  Valiant 
PAC Learnability of Finite Hypothesis Classes, Empirical Risk Minimization 
Chapter 3 of UML book 

Agnostic PAC Learnability of Finite Hypothesis Class, Uniform Convergence 
Chapter 4 of UML book 

No Free Lunch Theorem 
Chapter 5 of UML book 

VC dimension, Sauer Lemma, Growth Function 
Chapter 6 of UML book 

Fundamental Theorem of Statistical Learning Theory 
Chapter 6 of UML book 

Nonuniform Learnability, Structural Risk Minimization 
Chapter 7 of UML book 

Weak Learnability, Adaboost 
Chapter 10 of UML book 
Adaboost by Prof. Robert Schapire 
Online Learning, Mistake Bounds 
Chapter 21 of UML book 
Talk by Prof. Avrim Blum 
Littlestone Dimension, Regret Bounds 
Chapter 21 of UML book 

Expert Advice, Weighted Majority Rule 
Chapter 21 of UML book 

Perceptron Learning 
Chapter 21,of UML book 