CS 6804: Learning and
Sequential Decision-Making
Spring 2013
This site is basically a public
placeholder. The actual course site is on Piazza and can be accessed
here.
This course will study, from a computer science perspective, the
problems faced by agents who are situated in dynamic, changing
environments. The central question is how agents should reason in
order to make the best decisions in these environments. In order to do
so, they must be able to learn from experience and perform complex
decision-making tasks under uncertainty. Topics to be covered include,
but are not limited to, Markov decision processes, partial
observability, reinforcement learning, bandit problems, sequential
search, and reasoning and learning in games.
Class Meeting Times
MW 4:00-5:15PM
McBryde 210