#### Machine Learning Theory

##### (STATS214/CS229M, Fall 2022)

- Instructor: Tselil Schramm
- Time: Mondays & Wednesdays, 13:30–14:50 Pacific
- Location: 530-127
- TAs: John Cherian, Yash Nair, Asher Spector, and Yu Wang
- Office hours: Monday 17:30 in 160-B40 (Yash), Tuesday 17:30 in 200-303 (Asher), Wednesday 15:30 in TBD (John), Thursday 10:00 in Sequoia Hall 220 (Yu)

**Resources:**
We will have no official course text, but you may find the following resources useful:

- Lecture notes from the previous iteration of this course, taught by Tengyu Ma.
- Lecture notes from the previous iteration of this course, taught by Percy Liang.
- "High-dimensional statistics: a non-asymptotic viewpoint" by Martin Wainwright, available for free with your Stanford ID.
- "Convex Optimization" by Boyd and Vandenberghe, freely available online.

**Prerequisites:**
** Course Policies: ** A detailed overview of course policies (including grading and assignments) can be found in the course syllabus.

##### Course description

The goal of this course is to give mathematical tools for understanding machine learning.
We will explore the following questions:
How do machine learning algorithms work?
How can we quantify their success?
How much data do we need in order to guarantee that we actually learned something?
This is a theoretical, proof-based course, and our focus will be on algorithms with rigorous guarantees wherever they are to be had.

##### Lectures and Reading

Below is a preliminary schedule (subject to change), including the readings relevant to each lecture.