Statistical Machine Learning

10-702/36-702, Spring 2012

Larry Wasserman

Class Assistant: Sharon Cavlovich
Teaching Assistants: Martin Azizyan , Sivaraman Balakrishnan


Lecture:
Date and Time: Tuesday and Thursday, 12:00 - 1:20 am
Location: SH 125


Home Lectures

Week Date Day Lecture Topic Due
1 Jan
17		 Tuesday 1
 Concentration of Measure
 
Jan
19		 Thursday 2
 Concentration of Measure  
2 Jan
24		 Tuesday 3
 Concentration of Measure
 
Jan
26		 Thursday 4
 Convexity Hwk 1
(Due Friday)
3 Jan
31		 Tuesday 5
 Optimization  
Feb
2		 Thursday 6
 Nonparametric Density Estimation  
4 Feb
7		 Tuesday 7
 Nonparametric Density Estimation  
Feb
9		 Thursday 8
 NO CLASS
 Hwk 2
(Due Friday)
5 Feb
14		 Tuesday 9
 Nonparametric Regression
 
Feb
16		 Thursday 10
 Nonparametric Classification
Project proposals (due Friday) 
6 Feb
21		 Tuesday 11
 Minimax Theory  
Feb
23		 Thursday 12
 Minimax Theory
Hwk 3
(Due Friday)
7 Feb
28		 Tuesday 13
 Nonpar Bayes
 
Mar
1		 Thursday 14
 Nonparametric Bayes
 
8 Mar
6		 Tuesday 15
 Graphical Models  
Mar
8		 Thursday   Midterm exam
 
9 Mar
13		 Tuesday   Spring break; no class
Mar
15		 Thursday
10 Mar
20		 Tuesday 16
 Graphical Models
 
Mar
22		 Thursday 17
 Surrogate Loss Functions  
11 Mar
27		 Tuesday 18
 Mixtures and EM  
Mar
29		 Thursday 19
 Simulation Hwk 4
(Due Friday)
12 Apr
3		 Tuesday 20
 Random Projections
 
Apr
5		 Thursday 21
 RKHS  
13 Apr
10		 Tuesday 22
 Clustering  
Apr
12		 Thursday 23
 Clustering Project progress report
(Due Friday)
14 Apr
17		 Tuesday 24
 Manifolds  
Apr
19		 Thursday 25
 CARNIVAL: NO CLASS Hwk 5
(Due Friday)
15 Apr
24		 Tuesday 26
 Sparsity  
Apr
26		 Thursday 27
 Random Matrices  
16 May
1		 Tuesday 28
 Semi-Supervised Learning Project spotlights
May
3		 Thursday 29
 Student project spotlights Hwk 6
(Due Friday)
Final projects due Tuesday, May 8