Decision-Making Under Uncertainty

MSc-level course at DTU with around 150 students.

One of the aims of the course is to bring together approaches that are often developed in different communities (e.g., Operations Research, Control, AI) under a common modeling and evaluation perspective. The focus is on understanding how these methods connect and differ in practice, with particular emphasis on applying them to real-life decision problems.

Recorded Lectures: https://www.youtube.com/playlist?list=PLwngpBwa_6_IGlQ3v00Qm1YZXfODHpJHf

We cover:

1: Recap on Mathematical Optimization  

2: Markov Decision Processes

3: Model Predictive Control

4: 2-stage Stochastic Optimization

5: Multi-stage Stochastic Optimization

6: Dynamic Programming Fundamentals

7: Approximate Dynamic Programming

8: Approximate Dynamic Programming continued

9: Learn to Optimize

10: Heuristics

11: Environment and Policy Evaluation

12: Distributed Optimization & Decomposition