10/36-702 Statistical Machine Learning

Instructor: Larry Wasserman
Lecture Time: Tuesday and Thursday 1:30 - 2:50
Lecture Location: WEH 7500

Office Hour: Tuesdays 12:00 - 1:00 Baker Hall 132G
Office: Baker Hall 132G
Email: larry@stat.cmu.edu

TA Information

Collin Eubanks (Head TA)
Email: ceubanks@andrew.cmu.edu
Office Hours: Thursdays 12-1 BH 232K

Chenghui Zhou
Email: chenghuz@andrew.cmu.edu
Office Hours: Wednesdays 1:30-2:30 BH 232K

Hongyang Zhang
Email: hongyanz@andrew.cmu.edu
Office Hours: Wednesdays 9-10 GHC 8221

Yu Chen
Email: yuc2@andrew.cmu.edu
Office Hours: Thursdays 3-4 BH 232K

Pratice Problems

Practice problems for test


Click here for syllabus

Course Description

This course is an advanced course focusing on the intsersection of Statistics and Machine Learning. The goal is to study modern methods and the underlying theory for those methods. There are two pre-requisites for this course:
36-705 (Intermediate Statistical Theory)
10-715 or 10-701 (Introduction to Machine Learning)

Lecture Notes

Density Estimation
Nonparametric Prediction
Random Forests
Linear Regression
Graphical Models
Causal Inference
Minimax Theory
Nonparametric Bayesian Inference
Optimal Transport and Wasserstein Distance
Differential Privacy
Two Sample Testing

Additional Notes (Optional: not covered in class)

Linear Classifiers
Function Spaces


Assignments are due on Fridays at 3:00 p.m. Upload your assignment in Canvas.
Homework 1: Due Friday, Feb 2, by 3:00 pm (submit pdf on canvas)
Homework 2: Due Friday, Feb 23, by 3:00 pm (submit pdf on canvas)
Homework 3: Due Friday, March 30, by 3:00 pm (submit pdf on canvas)
Homework 4: Due Friday, April 20, by 3:00 pm (submit pdf on canvas)

No late assignments will be accepted. If you need an extension due to illness, email me BEFORE the deadline.


Homework 1 Solutions
Homework 2 Solutions
Homework 3 Solutions
Midterm Solutions
Homework 4 Solutions