Mon  Tue  Wed  Thu  Fri  
Week of Jan 9  RS 11:30 am  12:30 pm, CSA 251 
SA 2:153:00pm, CSA 201 
AR 3:00  4:00 pm, CSA 230 

Week of Jan 16  SA 23pm, CSA 201 
AR 34pm,CSA 230 

Week of Jan 23  GT 23pm, CSA 251 
RS 23pm, CSA 251 
AR 34pm,CSA 230 

Week of Jan 30  RS 23pm, CSA 251 
SA 23pm, CSA 201 AR 34pm,CSA 230 

Week of Feb 6  SA 34pm, CSA 201 GT 23pm, CSA 251 
AR 34pm, CSA 230 

Week of Feb 13  GT 1011am, CSA 251 

Week of Feb 20  GT 23pm, CSA 251 

Week of Mar 05  GT 23pm, CSA 251 
Date  Topic  References  Notes  
Part I: Supervised Learning Problems and Algorithms, Learning Theory  

1  Tu Jan 10  Course overview and introduction Binary classification, nearest neighbor 
[slides]  Assignment 1 out  
2  Th Jan 12  Decision trees  [HTF Sec 9.2] / [Bis Sec 14.4] / [DHS Sec 8.18.4] / [Mit Ch 3]* [MLSS'05 video lecture] 

3  Th Jan 19  Support vector machines  [HTF Ch 12] / [Bis Sec 7.1] / [DHS Sec 5.11] [E0 370 lecture notes] [SVM tutorial] Fast SVM training: [Platt, 1999] [Joachims, 1999] [Joachims, 2006] [ShalevShwartz et al, 2011] 

4  Tu Jan 24  Regression, least squares regression  [HTF Sec 3.13.4] / [Bis Sec 3.1]  Assignment 1 due Assignment 2 out 

5  Th Feb 2  Multiclass classification, ordinal regression, ranking  Multiclass SVMs: [Crammer & Singer, 2001] [Weston & Watkins, 1999] [Lee et al, 2004] Support vector ordinal regression: [Chu & Keerthi, 2007] Support vector ranking (RankSVM): [Joachims, 2002] [Herbrich et al, 2000] [Rakotomamonjy, 2004] 

6  Tu Feb 7  Generalization error and VCdimension  Generalization error: [E0 370 lecture 1 notes, Sec 2] [HTF Sec 2.4 early part] / [Bis Sec 1.5] VC dimension: [E0 370 lecture 4 notes, Sec 2 + Cor 3.3] Confidence intervals/bounds: [E0 370 lecture 3 notes, App A] Excess error/consistency: [E0 370 lecture 10 notes, Sec 12] 
Assignment 2 due  
7  Th Feb 9  PAC learning  [E0 370 lecture 11 notes] [E0 370 lecture 12 notes] 

Tu Feb 14  Midterm 1 (in class)  
8  W Feb 15  Boosting  [E0370 lecture 15 notes] [Bis Sec 14.3] / [HTF Ch 10,16] / [DHS Sec 9.5.2] 

Part II: Probabilistic Models for Supervised and Unsupervised Learning, Probabilistic Graphical Models 

9  Th Feb 16  Probabilistic models for regression  Bis Ch 3, Sec 2.3  Assignment 3 out  
10  Tu Feb 21  Generative models for classification  Bis Ch 4, Ch 2  
11  Th Feb 23  Discriminative models for classification 
Bis Ch 4, Ch 2


12  Fr Feb 24  Mixture Models and the EM algorithm  Bis Ch 9  
13  Tu Feb 28  Hidden Markov Models; ForwardBackward Algorithm  Bis Ch 13  Assignment 4 out  
14  Th Mar 1  Hidden Markov Models contd; Baum Welch Algorithm  Bis Ch 13  
15  Tu Mar 6  Intro to Graphical Models; Bayesian Networks 
Bis Ch 8


16  Th Mar 8  Markov Random Fields  Bis Ch 8  
17  Fr Mar 9  Relation between Directed and Undirected Models; Factor Graphs  Bis Ch 8  
18  Tu Mar 13  Exact Inference: Variable Elimination 
Lecture Notes


18  Th Mar 15  Exact Inference: Sumproduct Algorithm 
Bis Ch 8


Tu Mar 20  Midterm 2 (in class)  Solutions  
Th Mar 22  No Lecture  Assignment 5 out  
Tu Mar 27  No Lecture  
19  Th Mar 29  Discussion: Sampling from standard distributions  Bis Ch 11  
20  Tu Apr 3  Approximate Inference using Sampling; MCMC, Gibbs sampling 
Bis Ch 11


Part III: Additional Learning Settings and Applications  
21  Tu Apr 10  Online learning  [E0 370 lecture 18 notes]  Assignment 6 out  
22  Th Apr 12  Semisupervised and active learning  [Semisupervised learning literature survey] [Nigam et al, 2000] [Active learning literature survey] [CesaBianchi et al, 2004] 

23  Tu Apr 17  Guest lecture: Reinforcement learning Prof. B. Ravindran, IIT Madras 
Assignment 6 due  
24  Th Apr 19  Selected topics  
Fr Apr 27  Final exam (9 am  12 noon, CSA 117) 