Special Topics in Signal Processing〈Stochastic Control〉

Uncertainties exist everywhere in the real world, including finance, artificial intelligence (AI), and robotics. In the advent of increasing data, computation, and hardware, we are now more equipped than ever to design complex stochastic control systems that are far more robust, adaptive, and generalizable compared to their traditional deterministic counterparts. This project-based topics course will sample several of these important methods in stochastic control. Topics include stochastic dynamic programming, introductory stochastic differential/difference equations (SDEs), Markov chain models, stochastic programming, Bayesian filtering, and sampling.

prerequisite from another department: calculus, Ordinary Differential Equations and Dynamical Systems(Mathematical Sciences)

ETC: Python/MATLAB programming