Must be in the Graduate Program in Computer Science. All other graduate students must request permission.

This lecture-based, proof-oriented course focuses on foundational tools in learning theory, including generalization in the offline setting and regret bounds in the online setting, while also exploring active research directions. Machine learning theory asks questions such as: What guarantees can we prove for practical ML methods? What determines the inherent difficulty of different ML problems?