ECE 555
Spring 2011 - Tuesday & Thursday 3:30 - 4:50 241 EL
This course introduces both the analytical and computational aspects of stochastic control and performance evaluation, with applications to engineering, computer science, economics, and management science. Topics include Markov models and stochastic stability, development of control laws by dynamic programming, complete and partial information, Kalman filtering, and an introduction to machine learning. It is intended for graduate students who have some background in control and stochastic processes. Experience with Matlab is also desirable.
Course Outline: html pdf
Prerequisites
Some exposure to control and optimization at the level of ECE 415, and exposure to stochastic processes at the level of ECE 434 or MATH 366 (or consent of instructor.)
Grading
Resources
The following texts are on reserve:
- P. R Kumar, Stochastic systems: Estimation, identification, and adaptive control
- Torsten Soderstrom, Discrete-time stochastic systems: estimation and control
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