CS 480 / 001
MeetsWednesdays, 4:30 to 7:10 PM, in Lecture Hall 2.
PrerequisitesCS330 and CS310, no exceptions.
BookCS480 does not require a textbook, but it is very strongly recommended that you pick up the following text: ANSI Common Lisp by Paul Graham. We will be referring to that text in class. (BTW, if you're way too much into Common Lisp, you might also pick up this too).
Ordinarily CS 480 would also require Artificial Intelligence: A Modern Approach by Russell and Norvig, generally considered the best AI book, but I'm going to try going only with lecture notes. If you're into AI for the long haul, you might get the book anyway.
About the ClassThis course will begin by covering the basics of Lisp and the philosophy of Artificial Intelligence, plus discussion of simple systems, architectures, and platforms (robotics, etc.). From there we will discuss methods in learning (neural networks, decision trees, optimization, and time permitting, reinforcement learning). Then the course will turn to issues in problem solving and search, game design, and representation.
This course will be very challenging but (I hope!) interesting and eye-opening. Artificial Intelligence is a broad interdisciplinary field with a strong tradition in exploratory programming. You are expected to know the material in CS310 and CS330 well, and be able to get up to speed rapidly doing software development with strange new programming languages. Learning Lisp is a nontrivial endeavor. You should also be prepared to discuss and think about philosophical issues and be able to draw ideas from areas outside of computer science.
Course Web Pagehttp://cs.gmu.edu/~sean/cs480/
GradingGrading will be divided roughly as follows: 25% Midterm, 25% Non-cumulative Final Exam, 50% Course Assignments.
Honor CodeThe class enforces the GMU Honor Code, and the more specific honor code policy special to the Department of Computer Science. You will be expected to adhere to this code and policy.
DisabilitiesIf you have a documented learning disability or other condition which may affect academic performance, make sure this documentation is on file with the Office of Disability Services and come talk to me about accommodations.
Course Outcomes1. A knowledge of basic uninformed and heuristic search techniques. 2. A knowledge of basic logic or probabilistic reasoning techniques. 3. A knowledge of basic machine learning techniques. 4. An ability to implement basic AI methods in Lisp or in Prolog. 5. An ability to identify and apply an appropriate AI method to a given problem.