This course tackles some of the fundamental problems at
the interface of learning, decision theory and probability. The
course develops the theoretical foundations, representations and
algorithms for active learning, value of information problems,
experimental design, attention, optimal control and reinforcement
learning. The course will present these developments in the the
context of web crawling, relevance feedback in HCI, robotic
exploration, question answering systems, clinical trials, active
vision, active labelling of database entries, network problems,
graphics and animation, control, surveillance and more.
When: T-Th 2:00-3:30pm Where: Dempster
Nando de Freitas (nando at
cs) Nando's Office hours:
Mon (10:30am-12), Tue
(11am-12) (cicsr 183).
Intelligence: A Modern Aproach
by Stuart Russell and
by Martin Puterman.
by Dimitri Bertsekas and John
Reinforcement Learning: An Introduction
Sutton and Andrew Barto.
Programming and Optimal Control
by Dimitri Bertsekas.
Statistical Decision Theory and Bayesian Analysis
Grading Assignments: 20
Midterm : 30 Project: 30 Project presentation: 20
Assignments will involve both written and matlab
programming problems. All assignments are due on the
specified time. 20% off for each day late. Assignments will not
be accepted after 5 days late. Newsgroup: