MS&E351 Dynamic Programming and Stochastic Control


sequential decision illustration 


This course treats models, analysis, and algorithms pertaining to sequential decision problems. The focus is on Markov decision processes, with coverage of general theory, algorithms for cases with tractable finite state and action spaces, and problem-specific structural analysis and algorithm design beyond that.

It is assumed that students have basic knowledge of optimization (e.g., MS&E211) and probability (e.g., MS&E221) and are comfortable reading and writing formal mathematical proofs along the lines of what might be done in a real analysis course (e.g., Math 171).



Det er ganske sandt, hvad Philosophien siger, at Livet maa forstaaes baglænds. Men derover glemmer man den anden Sætning, at det maa leves forlænds.