15-884 Special Topic: Theoretical and empirical foundations of modern machine learning

12 units, Carnegie Mellon University

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 indistribution generalization, outofdistribution generalization, selfsupervised 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.

Prerequisites: none listed

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