Welcome to CS472 Foundations of Artificial Intelligence!
Who, When, Where?
Where: Upson Hall, Room B17
When: 11:15am-12:05am on Mondays, Wednesdays and Fridays
Professor: Claire Cardie
TAs: Kevin O'Neill, Jason Rohrer, Eric Strong, Kamen Yotov
Office hours and contact information can be found on the personnel
Final Exam: Thursday, December 14,
12:00pm-2:30pm, Ives 305
This course introduces the theoretical and computational techniques
that serve as a foundation for the study of artificial intelligence
(AI). Topics to be covered include the following:
- Introduction of AI and background: What is AI? Related fields
- Problem solving by search: principles of search, uninformed
(“blind”) search, informed (“heuristic”) search, game playing
- Logical knowledge representation and reasoning: knowledge bases
and infer-ence; constraint satisfaction; planning
- Knowledge-based systems and probabilistic reasoning: review
of probability theory; probabilistic knowledge representation
and reasoning; representing and handling uncertainty; expert system
architectures; bayesian networks
- Learning: inductive learning, concept formation, decision tree
learning, statisti-cal approaches, neural networks
- Natural language understanding: syntactic processing, ambiguity
resolution, text understanding
This course has no prerequisites other than a facility with programming
and the basic mathematical skills obtained in CS280.
An understanding of inference in first-order logic and basic blind
search techniques (i.e., breadth-first and depth-first search) is
also assumed, but background readings in these topics can be provided
for those with a deficiency in this area.
Primary textbook for the course is "Artificial Intelligence:
A Modern Approach", Russell and Norvig, Prentice-Hall, Inc.,
1995. Here are some possible locations where you can get one:
The PC’s in the Undergraduate PC Lab (Room 317, Upson Hall) are
the primary computing resource for the class.
Class Notes and Handouts
Most class notes and handouts will be available on-line on the
course materials page.
You are responsible for knowing and following Cornell’s academic
integrity policy. In short, the work you submit is expected
to be your own. Collaboration is allowed as prescribed above, but
you cannot copy all or part of another student’s homework or program
- regardless of whether that copy is on paper or on-line. Violation
of the Academic Integrity Code very often results in failure in
the course. If there is any doubt as to what kind of collaboration
is allowed, please ask the instructor.