Statistical Machine Learning

10-702/36-702, Spring 2016


LECTURES
Date and Time: Tuesday and Thursday, 1:30 - 2:50 pm
Location: HH B103

Larry Wasserman
Office Hours: Tuesdays 3:00-4:00 Baker Hall 132F

Class Assistant: Mallory Deptola (GHC 8001) 268-5527

Teaching Assistants:
Jisu Kim
Adams Yu
Bryan Hooi

TA Office hours:
Adams: Wednesday 10:30-11:30 GHC 8208
Bryan: Thursday 9:30-10:30 PH 117
Jisu: Friday 10:00-11:00 Baker Hall 132M



Statistical Machine Learning is a second graduate level course in advanced machine learning, assuming students have taken Machine Learning (10-715) and Intermediate Statistics (36-705). The course covers methodology and theoretical foundations.

Make sure you read the syllabus.



PIAZZA: Class discussion can be found at piazza


HOMEWORK:


Hand in homeworks to Mallory Deptola (GHC 8001)
Assignment 1: Due Friday Jan 29


Assignment 2: Due Friday Feb 19


Assignment 3: Due Friday March 18

Assignment 4: Due Friday April 15


SOLUTIONS:

Assignment 1 Solutions

Assignment 2 Solutions

Assignment 3 Solutions

Midterm Solutions

Assignment 4 Solutions


VIDEOS:

Video Lecture 1

Video Lecture 2

Video Lecture 3

Video Lecture 4

Video Lecture 5

Video Lecture 6

Video Lecture 7

Video Lecture 8

Video Lecture 9

Video Lecture 10

Video Lecture 11

Video Lecture 12

Video Lecture 13

Video Lecture 14

Video Lecture 15

Video Lecture 16

Video Lecture 17

Video Lecture 18

Video Lecture 19

Video Lecture 20

Video Lecture 21

Video Lecture 22

Video Lecture 23

Video Lecture 24




PRACTICE PROBLEMS FOR TEST

Practice Problems

Solutions

HANDOUTS:

Syllabus

Review

Function Spaces

Concentration of Measure

Linear Regression

Nonparametric Regression

Linear Classification

Nonparametric Classification

Minimax Theory

Density Estimation

Nonparametric Bayes

Clustering

Graphical Models

Dimension Reduction

Random Matrix Theory


TUESDAY THURSDAY FRIDAY



Jan 12 Jan 14 Jan 15
Review Function Spaces



Jan 19 Jan 21 Jan 22
Concentration of Measure Concentration of Measure



Jan 26 Jan 28 Jan 29
Linear Regression Linear Regression Assignment 1 Due



Feb 2 Feb 4 Feb 5
Nonparametric Regression Nonparametric Regression



Feb 9 Feb 11 Feb 12
Linear Classification Nonparametric Classification
Project Proposal Due



Feb 16 Feb 18 Feb 19
Minimax Theory Minimax Theory Assignment 2 Due



Feb 23 Feb 25 Feb 26
Nonparametric Bayes Boosting



March 1 March 3 March 4
Clustering Spring Break



March 8 March 10 March 11
Spring Break Spring Break Spring Break



March 15 March 17 March 18
Clustering Clustering Assignment 3 Due



March 22 March 24 March 25
Review MIDTERM



March 29 March 31 April 1
Graphical Models Graphical Models Progress Report Due



April 5 April 7 April 9
Graphical Models Graphical Models



April 12 April 14 April 15
Dimension Reduction No Class Assignment 4 Due



April 19 April 21 April 22
Random Matrix Theory Differential Privacy



April 26 April 28 April 29
Course Conference Course Conference