15-789 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 modern machinery including large language models and other foundation models what works and why How do we make things more reliable and robust We build a conceptual understanding of deep learning and foundation models through several different angles standard indistribution generalization, outofdistribution generalization, selfsupervised learning, data curation, scaling laws, alignment 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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