Machine Learning, Probability Modeling, Optimization, Search.
My long term goal is to develop systems that learn predictive models from massive data sources when the requisite models are complex (e.g., as in perception, language interpretation, information extraction, bio-informatics, robot learning). Some of the key challenges are knowledge representation for learning -- how to usefully express and debug prior domain assumptions -- and navigating complex model spaces -- how to find good models while avoiding over/under-fitting. Some ongoing projects include: statistical natural language modeling, reinforcement learning, and learning search control. I've also developed some new methods for probabilistic inference, optimization, and constraint satisfaction.
Formal grammars; relationship between grammars and automata; regular expressions; finite state machines; pushdown automata; Turing machines; computability; the halting problem; time and space complexity. Prerequisites: CMPUT 204, one of CMPUT 229, E E 380 or ECE 212 and one of MATH 225, 227, or 228 or consent of the instructor.