Instructor: Pedro Felzenszwalb
Email: pff (at) brown.edu
Office: Barus & Holley 355
Office hours by appointment
Homework 2
Due Wednesday February 22 by 4pm
Data for programming assignment
Homework 3
Due Friday March 9 by 4pm
Homework 4
Due Friday April 6 by 4pm
Data for programming assignment
Homework 5
Due Thursday May 3 by 4pm
Data for programming assignment
Lecture  Date  Topic  Reference (book sections) 

1 
January 26 
Introduction and overview 

2 
January 31 
Linear models for regression, basis functions, least squares 
1.1, 3.1 
3 
February 2 
Linear models for regression, regularization, probabilistic perspective 
3.1.1, 3.1.2, 3.1.4 
4 
February 7 
Classifiers, decision theory 
1.5, 1.5.1 
5 
February 9 
Decision theory, loss functions 
1.5.2 
6 
February 14 
Naive bayes classifier, ML estimation 
4.2.2, 4.2.3 
7 
February 16 
Linear threshold classifier, Perceptron 
4.1.7 
8 
February 23 
Linear Support Vector Machines 
7.1, 7.1.1 
9 
February 28 
Generalization bounds, VCdimension 
7.1.5 
10 
March 1 
Kernels 
6, 6.1 
11 
March 6 
Sequential data 
13, 13.1 
12 
March 8 
Hidden Markov Models 
13.2 
13 
March 13 
HMM computation (forward/backward) 

14 
March 15 
HMM computation (viterbi) 

15 
March 20 
Bayesian networks 
8.1, 8.2 
16 
March 22 
Bayesian networks 

17 
April 3 
Markov Random Fields 
8.3.1, 8.3.2, 8.3.4 
18 
April 5 
MRFs for image analysis 
8.3.3 
19 
April 10 
Markov chains 
11.2 
20 
April 12 
Gibbs sampling 
11.3 
21 
April 17 
Clustering, kmeans 
9.1 
22 
April 19 
Mixtures of gaussians 
9.2 
23 
April 24 
Expectation Maximization 
9.4 
24 
April 26 
Principal Component Analysis 
12.1 
25 
May 1 
Linear Discriminant Analysis & Random Projections 