Welcome to Theoretical and Empirical Foundations of Modern Machine Learning (15-884), Fall 2022!

Instructor: Aditi Raghunathan (raditi at cmu dot edu)

TA: Christina Baek (kbaek at cs dot cmu dot edu)

Lectures: Tuesday, Thursday 4:40-6:00pm at GHC 4102

In this advanced machine learning seminar class, we tackle the typical struggle in using the powerful deep learning machinery: what works and why? We build a conceptual understanding of deep learning through several different angles: standard in-distribution generalization, out-of-distribution generalization, self-supervised learning, scaling laws, memorization etc. We will read papers that contain a mix of theoretical and empirical insights with a focus on making connections to classic ideas, identifying recurring themes, and discussing avenues for future developments. The class aims to equip students with the ability to critically reason about and build a more principled understanding of current advances which will hopefully spark their own research.

This course combines lectures with paper presentations by the students, encouraging both fundamental knowledge acquisition as well as open-ended discussions and new research directions. The lectures will briefly introduce the main concepts, summarize a few key papers and connect to classical ideas if applicable.

The paper discussions will involve role-playing student seminars inspired by Alec Jacobson and Colin Raffel. We will be adopting the following roles.