Description: Lecture, three hours; discussion, one hour. Requisite: course 100A. Recommended preparation: experience with Python. Formulation of decision making problem as probabilistic inference. Derivation algorithms for solving probabilistic decision making. Implementation of code that executes inference and decision. Covers Markov decision process, planning, search, and reinforcement learning. Letter grading.

Units: 4.0