CS 6781 - Theoretical Foundations of Machine Learning

Spring. 4 credits. Letter grades only.

Prerequisite: CS 4820 for undergraduate students.

N. Haghtalab.

This course will cover fundamental topics in theory of machine earning for modern use, including statistical, computational, and social consideration. We start with a basic statistical and computational toolset required for understanding machine learning. We then explore a number of modern perspectives on machine learning including connections between game theory and machine learning, robustness of machine learning to adversaries, a beyond the worst-case analysis perspective on learning, learning from social and strategic behavior, role of learning in algorithm design, and ethics in machine learning. In addressing these, the course makes connections to statistics, algorithms, complexity theory, optimization, game theory, and more.