| Lecture | Date | Topic | Reference (book sections) |
|---|---|---|---|
1 |
January 22 |
Introduction |
|
2 |
January 29 |
Linear regression, basis functions, least squares |
1.1, 3.1 |
3 |
February 3 |
Maximum likelihood view of linear regression, outliers |
3.1 |
4 |
February 5 |
Robust regression via Linear Programming |
|
5 |
February 10 |
Classification, Bayesian Decision Theory, MLE for Bernoulli |
1.5.2, 2.1 |
6 |
February 12 |
MLE for Bernoulli, Multinomial, Multivariate Gaussian |
2.1, 2.2, 2.3 |
7 |
February 19 |
Bayesian estimation and predictive distribution |
2.1, 2.2, 2.3 |
8 |
February 24 |
Linear separators, perceptron algorithm |
4.1, 4.1.7 |
9 |
March 3 |
Max margin separators, linear support vector machines |
7.1 |
10 |
March 5 |
Gradient descent for linear SVM, Multiclass problems |
7.1 |
11 |
March 10 |
Kernel Methods |
6, 6.1, 6.2, 7.1 |
12 |
March 12 |
PAC learning, finite hypothesis spaces, Boolean functions |
[1] |
13 |
March 17 |
PAC learning threshold functions, infinite hypothesis spaces, VC dimension |
[1] |
14 |
March 31 |
Bayesian Networks |
8.1 |
15 |
April 2 |
Bayesian Networks |
8.1 |
16 |
April 7 |
Hidden Markov Models |
|
17 |
April 9 |
Hidden Markov Models |
|
18 |
April 14 |
Clustering, K-means |
9.1, 9.2 |
19 |
April 16 |
Mixture of Gaussians, EM |
9.4 |
20 |
April 21 |
PCA, LDA |
12.1, 4.1.4 |
21 |
April 23 |
Parzen windows, Nearest-neighbor methods |
|
22 |
April 28 |
Neural Networks |
[1] An Introduction to Computational Learning Theory. Kerns and Vazirani.