Lecture

Date

Topics Covered

Lecture
1

April 1

Mistake bound model, Halving algorithm, Linear classifiers and margin

Lecture
2

April 3

Perceptron algorithm, Lower bound for L2margin, Winnow

Lecture
3

April 7

Winnow (contd.), Online Convex Programming, Online Gradient Descent

Lecture
4

April 9

Exponentiated Gradient Descent, Applications of Online Convex
Programming

Lecture
5

April 14

Proof of von Neumann's Minmax Theorem, Weak and Strong Learning,
Boosting

Lecture
6

April 16

AdaBoost, L1 Margins and Weak Learning

Lecture
7

April 21

Probabilistic Setup, Loss functions, Empirical Risk Minimization (ERM)

Lecture
8

April 23

Concentration, ERM, Compression Bounds

Lecture
9

April 28

Compression Bounds (contd.), Rademacher averages

Lecture
10

April 30

Massart's Finite Class Lemma, Growth Function

Lecture
11

May 5

VC Dimension, Sauer's Lemma

Lecture
12

May 7

VC Dimension of Multilayer Neural Networks, Range Queries

Lecture
13

May 12

Online to Batch Conversions

Lecture
13a

Supplementary Notes

(Exponentiated) Stochastic Gradient Descent for L1 Constrained Problems

Lecture
14

May 14

Covering Numbers and Rademacher Averages

Lecture
15

May 19

Dudley's Theorem, Pseudodimension, Fat Shattering Dimension, Packing
Numbers

Lecture
16

May 21

Fat Shattering Dimension and Covering Numbers

Lecture
17

May 26

Rademacher Composition and Linear Prediction
