## 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

## Syllabus

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

Review

Density Estimation

Nonparametric Regression

Linear Regression

Sparsity

Nonparametric Sparsity

Linear Classifiers

Nonparametric Classifiers

Random Forests

Clustering

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

Boosting

Support Vector Machines

Online Learning

## Assignments

Assignments are due onFridays 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)

## Solutions

Homework 1 Solutions

Homework 2 Solutions

Homework 3 Solutions

Homework 4 Solutions