Class 
Date 
Sections 
Slides 
Audio 
Topic 
01 
Wednesday, January 07, 2009 
01.01  01.04 
01.01 
01.11

01 
Course Introduction 
02 
Friday, January 09, 2009 
01.05  02.02 
01.12 
02.05

02 
Introduction, Probability Decision Theory 
03 
Monday, January 12, 2009 
02.03 
02.06 
02.22 
02b 
Bayes Decision Theory 
04 
Wednesday, January 14, 2009 
02.04 
03.01 
03.15

03 
Multivariate Gaussian Densities 
05 
Friday, January 16, 2009 
02.05, 02.06 
03.16 
04.07

04 
Transformations, Threshold Decoding 
N/A 
Monday, January 19, 2009 
N/A 
N/A 
N/A 
Holiday: Martin Luther King Day 
06 
Wednesday, January 21, 2009 
02.07  02.12 
04.08 
05.02

04b 
Error Bounds 
07 
Friday, January 23, 2009 
03.01  03.02 
05.03 
05.11

05 
Maximum Likelihoood Estimation 
08 
Monday, January 26, 2009 
03.02 
05.12 
06.10

06 
Variance Relationships 
09 
Wednesday, January 28, 2009 
03.03 
06.11 
06.19

06b 
Bayesian Estimation 
10 
Friday, January 30, 2009 
03.04 
07.01 
07.11

07 
Multivariate Gaussian Case 
11 
Monday, Feburary 02, 2009 
03.05  03.07 
07.13 
08.04

08 
Sufficient Statistics, Dimensionality 
12 
Wednesday, February 04, 2009 
03.08 
08.05 
08.10

08b 
Component Analysis 
13 
Friday, February 06, 2009 
03.08 
09.01 
09.09

09 
Discriminant Analysis 
14 
Monday, February 09, 2009 
10.13.3 
10.01 
10.08

10 
Heteroscedastic LDA and ICA 
15 
Wednesday, Feburary 11, 2009 
03.09 
11.01 
11.12

11 
The Expectation Maximization Algorithm 
16 
Friday, February 13, 2009 
03.10 
12.01 
12.09

12 
Hidden Markov Models  The Basics 
17 
Monday, February 16, 2009 
03.10 
12.10 
13.04

13 
Parameter Estimation in Hidden Markov Models 
18 
Wednesday, Feburary 18, 2009 
03.10 
13.04 
13.08

13b 
Continuous Distribution Hidden Markov Models 
19 
Friday, February 20, 2009 
03.10 
13.08 
13.08

13c 
Application of HMMs 
20 
Monday, February 23, 2009 
04.01  04.03 
14.01 
14.08

14 
Parzen Window 
21 
Wednesday, February 25, 2009 
01.01  03.10 
MidTerm 
N/A 
MidTerm 
22 
Friday, Feburary 27, 2009 
04.04  04.07 
14.09 
16.02

16 
NearestNeighbor Classification, Risk Minimization 
23 
Monday, March 02, 2009 
05.01  05.03 
16.03 
16.11

16b 
Support Vector Machines 
24 
Wednesday, March 04, 2009 
05.04 
17.01 
17.06

17 
Neural Networks 
25 
Friday, March 06, 2009 
06.01  07.06 
17.07 
17.14

17b 
Backpropagation 
26 
Monday, March 09, 2009 
08.01  08.07 
18.01 
18.13

18 
Decision Trees 
27 
Wednesday, March 11, 2009 
09.01  09.02 
19.01 
19.06

19 
Foundations of Machine Learning 
28 
Friday, March 13, 2009 
09.03  09.04 
19.07 
19.13

19b 
Bias, Variance and Bootstrapping 
N/A 
Monday, March 16, 2009 
N/A 
N/A 
N/A 
Spring Break 
N/A 
Wednesday, March 18, 2009 
N/A 
N/A 
N/A 
Spring Break 
N/A 
Friday, March 20, 2009 
N/A 
N/A 
N/A 
Spring Break 
29 
Monday, March 23, 2009 
09.05 
20.01 
20.08

20 
Estimating and Comparing Classifiers 
30 
Wednesday, March 25, 2009 
09.06 
20.09 
21.07

21 
Combining Classifiers, Reinforcement Learning 
31 
Friday, March 27, 2009 
09.07 
21.08 
21.18

21b 
Markov Decision Processes 
32 
Monday, March 30, 2009 
10.01, 10.02 
22.01 
22.06

22 
Mixture Densities 
33 
Wednesday, April 01, 2009 
10.04, 10.05 
22.07 
23.04

23 
Unsupervised Bayesian Learning 
34 
Friday, April 03, 2009 
10.05, 10.06 
23.05 
23.14

23b 
Hierarchical Clustering 
35 
Monday, April 06, 2009 
10.07  10.12 
24.01 
24.06

24 
OnLine Clustering 
36 
Wednesday, April 08, 2009 
10.13, 10.14 
24.07 
25.05

24b 
Discriminative Training 
N/A 
Friday, April 10, 2009 
N/A 
N/A 
N/A 
Holiday: Good Friday 
37 
Monday, April 13, 2009 
N/A 
25.06 
26.05

25 
Particle Filters 
38 
Wednesday, April 15, 2009 
N/A 
26.06 
26.16

26 
Monte Carlo Methods, Posterior Estimation 
39 
Friday, April 17, 2009 
N/A 
27.01 
27.05

27 
PACBayes Bound 
40 
Monday, April 20, 2009 
N/A 
27.06 
28.02

27b 
Applications of the PACBayes Bound 
41 
Wednesday, April 22, 2009 
N/A 
28.02 
28.15

28 
Statistical Significance 
42 
Friday, April 24, 2009 
N/A 
29.01 
29.19

29 
Pattern Recognition Applications 
43 
Monday, April 27, 2009 
Comprehensive 
Final 
N/A 
Final Exam (8 AM  11 AM) 