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 page
Final Exam: Thursday, December 14, 12:00pm-2:30pm, Ives 305

Course Description

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

Prerequisites

This course has no prerequisites other than a facility with programming (e.g., CS211 or CS212) 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.

Text

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:

• Campus Store
• Amazon
• Barnes&Noble
• Borders

Machines

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.

Academic Integrity

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.