Description: Lecture, three hours; discussion, one hour (when scheduled). Requisite: course 31, or background in logic, computer science, statistics, or mathematics. Topics may include learning theory, statistical inference, causal inference, artificial intelligence, non-probabilistic approaches to inductive logic, or deeper study of topics from course C133B. May be repeated for credit with consent of instructor. P/NP or letter grading.

Units: 4.0