| 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 1-2 of Shai Shalev-Shwartz and Shai Ben-David 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 |