<html><body>Colloquium Speaker <br>Emma Brunskill , Carnegie Mellon University <br>Wednesday, November 19, 2014 - 4:30pm <br>Computer Science 105 <br><br>Interactive ML for People: The Small Data Problem <br><br>Consider
an intelligent tutoring system or an autonomous decision support tool
for a doctor. Though such systems may in aggregate have a huge amount of
data, the data collected for a single individual is typically very
small, and the policy space (of what to next teach a student or how to
help treat a patient) is enormous. <br><br>I will describe two machine
learning efforts to tackle these small data challenges: learning across
multiple tasks, and better use of previously collected task data, where
tasks in both cases involve sequential stochastic decision processes
(reinforcement learning and bandits). I will also present results of how
one of these techniques allowed us to substantially increase engagement
in an educational game to teach fractions. <br><br>Emma Brunskill is an
assistant professor in the computer science department at Carnegie
Mellon University. She is also affiliated with the machine learning
department at CMU. She works on reinforcement learning, focusing on
applications that involve artificial agents interacting with people,
such as intelligent tutoring systems. She is a Rhodes Scholar, Microsoft
Faculty Fellow and NSF CAREER award recipient, and her work has
received best paper nominations in Education Data Mining (2012, 2013)
and CHI (2014). <br><br></body></html>