# Teaching

**EE 556: Stochastic Systems & Reinforcement Learning**

Stochastic system models, Dynamic programming, Linear quadratic control, Kalman filtering and estimation, System identification, approximate dynamic programming methods, adaptive control, reinforcement and online learning.

**Last Taught: ****Spring 2021**

**EE 512: Stochastic Processes**

Probability theory and stochastic processes, including renewal theory, Markov chains, Brownian motion, martingales, and stochastic calculus. Applications in communication networks, queuing theory and financial systems.

**Last Taught: Spring 2017**

**EE 503: Probability for Electrical & Computer Engineers**

Rigorous coverage of probability, discrete and continuous random variables, functions of multiple random variables, covariance, correlation, random sequences, Markov chains, estimation, and introduction to statistics.

**Last Taught: Spring 2020**

**EE ****364****: Introduction to Probability and Statistics ****for Electrical Engineering and Computer Science**

Introduction to concepts of randomness and uncertainty: probability, random variables, statistics. Applications to digital communications, signal processing, automatic control, computer engineering and computer science.

**Last Taught: ****Spring 2019**

**EE 598: Electrical Engineering Research Seminar **

Introduction to research in electrical engineering. Topics vary by semester. May be repeated for up to one unit of credit for MS students, two units of credit for PhD students.

Registration Restriction: Open only to Masterâ€™s and Doctoral Students.

**Last Taught: Spring 201****9**

**EE 6****48****: Network Economics & Game****s**

Economics of networks; game theory, mechanism design and auctions in networks; spectrum sharing mechanisms in communications; pricing of differentiated services; network security.

**Last Taught: Fall 2010**