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, Kmeans 
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, Nearestneighbor methods 

22 
April 28 
Neural Networks 
[1] An Introduction to Computational Learning Theory. Kerns and Vazirani.