| Lecture | Date | Topic | Reference (book sections) |
|---|---|---|---|
1 |
January 24 |
Introduction |
|
2 |
January 29 |
Linear models for regression, basis functions, least squares |
1.1, 3.1 |
3 |
January 31 |
Linear models for regression, regularized least squares |
3.1.1, 3.1.2, 3.1.4 |
4 |
February 5 |
Classifiers, decision theory |
1.5, 1.5.1 |
5 |
February 7 |
Decision theory, MLE for Bernoulli distribution |
1.5.2, 2.1 |
6 |
February 12 |
MLE for multinomial and Gaussian, Conditional Gaussian models |
2.2, 2.3, 4.2.2 |
7 |
February 14 |
Discriminative models |
1.5.4, 4.2, 4.2.1 |
8 |
February 21 |
Logistic regression |
4.3.2, 4.3.3, 4.3.4 |
9 |
February 26 |
Features, linear separation, perceptron algorithm |
4.1, 4.1.7 |
10 |
February 28 |
Max-margin classifiers |
7.1 |
11 |
March 5 |
Hidden Markov models |
|
12 |
March 7 |
Hidden Markov models |
|
13 |
March 12 |
Bayesian networks |
Section 8 |
14 |
March 14 |
Bayesian networks |
Section 8 |
15 |
March 19 |
Ising model, MAP estimation, min-cuts |
Section 8.3, MAP estimation, Graph algorithms |
16 |
March 21 |
Ising model, MAP estimation, min-cuts |
|
17 |
April 2 |
PAC learning |
|
18 |
April 4 |
VC dimension |
|
19 |
April 9 |
More VC dimension and PAC learning examples |
|
20 |
April 11 |
Adaboost |
|
21 |
April 16 |
Unsupervised learning, mixture of Gaussians |
9.1, 9.2 |
22 |
April 18 |
Expectation-Maximization |
9.4 |
23 |
April 23 |
Nearest-neighbors |
|
25 |
April 25 |
Non-parametric estimation |