Markov Decision Processes

3 credits

Markov Decision Processes (MDPs) encapsulate a broad class of mathematical models for solving sequential decision problems under uncertainty. Combines techniques from linear/convex optimization, probability, statistics, and machine learning to build a modeling, theoretical, and algorithmic foundation for MDPs. Prerequisite: either IND E 508 and IND E 513, other similar classes in optimization and stochastic models, or permission of instructor. Coding experience with languages such as MATLAB or Python; recommended: graduate level optimization, probability, and statistics. Computer programming.