This course explores fundamental tools for analyzing and designing trustworthy machine learning systems through the lens of information theory, statistics, and learning theory. We study reliability guarantees for modern models by quantifying generalization, fairness, and privacy. We will also introduce practical aspects of watermarking and IP protection for large generative models.
Topics will include:
Information-theoretic foundations for generalization and sample complexity
Privacy (e.g., differential privacy) and leakage measures
Fairness constraints in learning
Watermarking for generative AI
This course builds conceptual connections between theoretical principles and real-world trustworthy AI challenges.
Prerequisites:
Graduate standing in CS/ECE/Statistics or instructor consent/approval.
Comfort with probability, linear algebra, and basic machine learning concepts is expected.
Once the quarter starts, instructor approval is required to maintain enrollment in the course, including if students do not have the listed pre-requisite courses completed.