By the end of the semester, a student is expected to be able to explain how to manage supply and demand in a power system through advanced control techniques, and to design and analyze innovative policy, regulation, and business models in order to implement the next-generation grid architectures. Read More

The first goal is to learn how to formulate models for the purposes of control, in applications ranging from finance to power systems to medicine. Linear and Markov models are chosen to capture essential dynamics and uncertainty. The course provides several approaches to design control laws based on these models,…... Read More

An introduction to stochastic process theory with emphasis on applications to communications, control, signal processing and machine learning.   The course covers basic models, including Markov processes, and how they lead to algorithms for classification prediction, inference and model selection. The course mainly follow's Hajek's excellent textbook.  Markov chains and Monte-Carlo…... Read More

Reinforcement learning is a collection of tools for the design of decision and control algorithms. What makes RL different from traditional control is that the modelling step is avoided, and instead the control design is based on observations of the system to be controlled. Read More