Statistical Machine Learning

10-702/36-702, Spring 2011

Aarti Singh and Larry Wasserman

Class Assistant: Michelle Martin
Teaching Assistants: T. K. Huang, Min Xu


Lecture:

Date and Time: Monday and Wednesday, 10:30 - 11:50 am
Location: 1305 NSH

Recitation: Date and Time: Thursday, 5 - 6 pm
Location: 1305 NSH


Home People Lectures

Week Date Day Lecture Topic Notes/Assignments Due
1 Jan
10
M 1
(L)
Concentration of Measure
( Recitation Notes 1 )
Syllabus
 
Jan
12
W 2
(L)
Concentration of Measure Hwk 1 Solution
 
2 Jan
17
M 3
(A)
Convexity I
(Recitation Notes 2)
   
Jan
19
W 4
(A)
Convexity II Some useful links for Fenchel duality:link1, link2 Hwk 1
(Friday)
3 Jan
24
M 5
(A)
Optimization Hwk 2 Solution
 
Jan
26
W 6
(L)
Nonparametric Density Estimation    
4 Jan
31
M 7
(L)
Nonparametric Regression    
Feb
2
W 8
(L)
Nonparametric Regression
( Recitation Notes: derivation of loo formula)
  Hwk 2
(Friday)
5 Feb
7
M 9
(A)
Nonparametric Classification
(Notes on Analysis of Histogram and Decision Tree Classifiers)
Hwk 3
Solutions
 
Feb
9
W 10
(L)
Nonparametric Bayes
( Recitation Notes 5 )
   
6 Feb
14
M 11
(L)
Minimax Theory   Project proposals 
Feb
16
W 12
(L)
Minimax Theory
(Recitation Notes 6)
  Hwk 3
(Friday)
7 Feb
21
M 13
(A)
Undirected graphical models
(Notes on undirected graphical model representation)
(Papers on structural consistency for Gaussian graphical model and Ising model)
(Recitation notes on the Glasso algorithm)
Hwk 4
 
Feb
23
W 14
(A)
Nonparametric graphical models
(Nonparanormal, Forest Density Estimation papers)

 
8 Feb
28
M 15
(A)
Surrogate loss functions    
Mar
2
W   Midterm exam
Solution
practice midterm  
9 Mar
7
M   Spring break; no class
Mar
9
W  
10 Mar
14
M 16
(L)
Simulation
   
Mar
16
W 17
(L)
EM/Variational   Hwk 4
(Friday)
11 Mar
21
M 18
(A)
Fast Rates for Classification
Recitation Notes 9
Hwk 5
Gibbs sampling Paper by Ishwaran-James
 
Mar
23
W 19
(A)
Random Projections    
12 Mar
28
M 20
(L)
RKHS
Recitation Notes 10
   
Mar
30
W 21
(L)
Random Matrix Theory   Hwk 5
(Friday)
13 Apr
4
M 22
(A)
Clustering  
Apr
6
W 23
(A)
Clustering   Project progress report
(Friday)
14 Apr
11
M 24
(L)
Manifold learning    
Apr
13
W 25
(L)
Sparsity and high dimensional inference Hwk 6
 
15 Apr
18
M 26
(L)
Sparsity and high dimensional inference    
Apr
20
W 27
(A)
Semi-supervised Learning    
16 Apr
25
M 28
(A)
Active Learning   Project spotlights
Apr
27
W 29
Student project spotlights   Hwk 6
(Friday)
Final projects due Tuesday, May 3