Dynamic Programming and Stochastic Control
MIT Learn · advanced
The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.
Skills covered
Disclaimer
Suggestions only — review each course yourself to judge whether it meets the role's requirements. Completing a course doesn't guarantee proficiency or that you'll qualify; hiring standards vary by employer.
We may earn a commission through some course links.