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 L2-margin, 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 Multi-layer 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
|