MSE 531 - Sequential Decision Making: Reinforcement Learning and Causal Inference

This course explores the intersection of sequential decision making, reinforcement learning (RL), and causal inference. Through theory, programming exercises, and real-world case studies, students learn to model, optimize, and explain decisions under uncertainty using reinforcement learning and causal frameworks. Topics include Markov Decision Processes (MDPs), value approximation, policy evaluation, Q-learning, and structural causal models.