36-708 Statistical Methods for Machine Learning

Instructor: Larry Wasserman
Lecture Time: Tuesday and Thursday 1:30 - 2:50
Lecture Location: POS 152

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

TA Information

Nic Dalmasso
Email: ndalmass@andrew.cmu.edu
Office Hours: Wednesdays 4-5 PH 223B

Boyan Duan
Email: boyand@andrew.cmu.edu
Office Hours: Thursdays 12-1 Baker Hall 132 Lounge


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)
36-707 (Regression)

Lecture Notes

Density Estimation
Nonparametric Regression
Linear Regression
Nonparametric Sparsity
Linear Classifiers
Nonparametric Classifiers
Random Forests
Graphical Models
Directed Graphical Models
Causal Inference
Minimax Theory
Nonparametric Bayesian Inference
Conformal Prediction
Differential Privacy
Optimal Transport and Wasserstein Distance
Two Sample Testing
Dimension Reduction
Support Vector Machines
Online Learning


Assignments are due on Fridays at 3:00 p.m. Upload your assignment in Canvas.

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

Homework 1 (due Friday Feb 1 3:00. Submit a pdf on Canvas)
Homework 2 (due Friday Feb 22 3:00. Submit a pdf on Canvas)
Homework 3 (due March 29 3:00. Submit a pdf on Canvas)
Homework 4 (due April 19 3:00. Submit a pdf on Canvas)


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