## 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

## 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)

10-715 or 10-701 (Introduction to Machine Learning)

## Lecture Notes

Review

Density Estimation

Nonparametric Prediction

Random Forests

Linear Regression

Clustering

Graphical Models

Causal Inference

Minimax Theory

Nonparametric Bayesian Inference

Optimal Transport and Wasserstein Distance

Differential Privacy

Manifolds

Two Sample Testing

## Additional Notes (Optional: not covered in class)

Linear Classifiers

Function Spaces

Sparsity

## Assignments

Assignments are due onFridays 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.

## Solutions

Homework 1 Solutions

Homework 2 Solutions

Homework 3 Solutions

Midterm Solutions

Homework 4 Solutions