This mini-course is intended to provide a short introduction to the
subject of learning in games – from models of evolution in population
games to online learning and regret minimization. The course will
consist of three main parts, depending on the type of
games considered – nonatomic, finite, or continuous. In particular, we
will cover the basics of evolutionary dynamics in population games
(insisting on the replicator equation and its rationality properties),
no-regret learning in finite games (focusing on
the multiplicative/exponential weights algorithm and its properties),
and the basics of online optimization in continuous games (online
gradient descent, follow the regularizer leader, etc.). We will also
discuss the impact of the information available to
the players, as well as a range of concrete applications to machine
learning and operations research.
Every Friday 16:00-19:00
Passcode: 54a8SP