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MSE 531Sequential Decision Making: Reinforcement Learning and Causal Inference

Units
0.50
Faculty
Engineering
Subject
Management Science and Engineering

When it runs

  1. F24
  2. W25
  3. S25
  4. F25
  5. W26
  6. S26
  7. F26
  8. W27
  9. S27

Ran in 1 of the last 7 terms. Winter 2027: not on the schedule. Spring 2027: schedule not out yet.

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.

Prerequisites

  • Complete all of the following
    • Must have completed at least 1 of the following:
      • AE224 - Probability and Statistics (0.50)
      • BME213 - Statistics and Experimental Design (0.50)
      • CHE220 - Probability and Statistics (0.50)
      • CIVE224 - Probability and Statistics (0.50)
      • ECE203 - Probability Theory and Statistics 1 (0.50)
      • ENVE224 - Probability and Statistics (0.50)
      • ME202 - Statistics for Engineers (0.50)
      • MSE251 - Probability and Statistics 1 (0.50)
      • MTE201 - Experimental Measurement and Statistical Analysis (0.50)
      • NE215 - Probability and Statistics (0.50)
      • STAT206 - Statistics for Software Engineering (0.50)
      • STAT231 - Statistics (0.50)
      • STAT241 - Statistics (Advanced Level) (0.50)
      • SYDE212 - Probability, Statistics, and Data Science (0.50)
    • Must have completed at least 1 of the following:
      • BME411 - Optimization and Numerical Methods (0.50)
      • CHE521 - Process Optimization (0.50)
      • CIVE332 - Civil Systems and Project Management (0.50)
      • CO250 - Introduction to Optimization (0.50)
      • ENVE335 - Decision Making for Environmental Engineers (0.50)
      • MSE331 - Introduction to Optimization (0.50)
      • MSE332 - Deterministic Optimization Models and Methods (0.50)
      • SYDE411 - Optimization and Numerical Methods (0.50)

Antirequisites

None listed.

Leads to

No course lists it as a requisite.

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Check it elsewhere for Fall 2026

Calendar versions

Undergraduate Calendar