The goal of the course is to familiarize students with the mathematical concepts and computational techniques for stochastic decision and optimization problems, and illustrate the application of these methods in various scenarios. The methodological framework of Markov decision processes and stochastic dynamic programming models will play a central role, and the students are expected to obtain knowledge of the main problem formulations and be able to apply the the main computational approaches in that domain to stylized problems.

You will learn of state-of-the-art approaches in:

Markov Decision Theory
Multi-Armed Bandits
Reinforcement Learning
Stochastic / Distributed / Robust Optimization