We consider how to learn through experience to make intelligent decisions.  In the generic setting, called the contextual bandits problem, the learner must repeatedly decide which action to take in response to an observed context, and is then permitted to observe the received reward, but only for the chosen action.  The goal is to learn to behave nearly as well as the best policy (or decision rule) in some possibly very large and rich space of candidate policies.  This talk will describe recent progress on this problem and some of its variants.

Light refreshments will be served before the lecture at 3:30 p.m.