Courses

Current courses, teaching philosophy, and past offerings at UNC Chapel Hill.

Fall 2026

UNC Chapel Hill

STOR 565 – Machine Learning

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.

October 9 syllabus: Foundations of learning from data; predictive modeling and regularization; flexible prediction and structure discovery; learned representations and generative AI. Mathematical analysis, Python experiments, and a final project connect these topics.

Visit the Fall 2026 course website · Course overview and public syllabus summary

STOR 323I – Machine Learning: Ethics and Society

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.

October 9 syllabus: Foundations and ethical issues; learning while acting; people, platforms, and rules; generative AI and society. Discussions, technical exercises, and a team project connect the models to responsibility, competing values, and possible interventions.

Visit the Fall 2026 course website · Course overview and public syllabus summary

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.

Predict, experiment, reflect: two public learning challenges. A synthetic regularization experiment and a fictional recommendation scenario let visitors try this approach without enrolling or submitting student information.

Teaching philosophy

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.

  • Connect principles with practice. Mathematical reasoning and implementation inform each other. Derivations, experiments, and projects give students opportunities to test their understanding and explain their results.
  • Connect algorithms with their consequences. Technical choices shape incentives, opportunities, and social outcomes. I bring ML tools and philosophical frameworks together so students can examine these effects and reason about trade-offs.
  • Develop independent judgment. Discussion, writing, and collaborative projects ask students to formulate questions, support an argument with evidence, and evaluate possible interventions.

Past offerings

These pages retain the syllabi and information for their respective semesters.