A mathematically grounded introduction to statistical machine learning for advanced undergraduate and master’s students. The course connects loss, optimization, and generalization with representative models, implementation, and modern applications.
Design outline: Foundations of learning from data; supervised learning and optimization; unsupervised and generative models; online learning and feedback-driven systems. Mathematical analysis, Python experiments, and a final project connect the ideas across these topics.
Visit the Fall 2026 course website · Course overview and outline
An undergraduate course connecting the technical foundations of ML with moral philosophy and the social and economic effects of algorithmic decisions. Students examine how systems affect people, how people respond, and what technical or policy interventions might improve the outcomes.
Design outline: Moral philosophy and the foundations of ML; fairness, privacy, and feedback loops; strategic behavior and platforms; generative AI and alignment. Structured discussions, case studies, technical exercises, and a team project develop students’ ability to reason about competing values and propose interventions.
Visit the Fall 2026 course website · Course overview and outline
Course materials may require sign-in or course access. Please use the course websites for current schedules and materials, and Canvas for enrolled-student submissions and grades.
My goal is to help students understand why a method works, what assumptions it makes, and how to judge its use in a particular setting. I use representative problems to connect ideas across machine learning and to prepare students to learn new methods independently.
These pages retain the syllabi and information for their respective semesters.