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CPSC 625-600 Artificial Intelligence:
Fall 2004

Syllabus

I. General Information

Instructor:

Dr. Yoonsuck Choe
Email: choe(a)tamu.edu
Office: HRBB 322B
Phone: 845-5466
Office hours: T/TR 10:30am-12:00pm. Other times: by appointment only.

TA:

Heejin Lim
Email: hjlim(a)cs.tamu.edu
Office: HRBB 322A
Phone: 845-5481
Office hours: M/TR 9:30--11:30 a.m. HRBB 322A

Prerequisite/Restrictions:

CPSC 311

Lectures:

T/TR 12:45pm-2:00pm, HRBB 113

Goals:

To understand the problems in AI and to learn how to solve them:

1. traditional methods in AI (search, pattern matching, logical inference, theorem proving, etc.).
2. modern approaches in AI (learning, probabilistic approaches, etc.).

Textbook:

Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (AIMA, hereafter), 2nd Edition, Prentice Hall, New Jersey, 2003.
ISBN 0-13-790395-2
Book Homepage
* The first edition may be okay if that's what you have.

Computer Accounts and Usage:

1. Computer accounts: if you do not have a unix account, ask for one on the CS web page. We will be using the CMU Common Lisp as our main language. Example code will only be made available in Lisp, and in general other languages will not be permitted.
2. CMU Common Lisp:
   • Carnegie Mellon U. Common Lisp homepage
   • On all SunOS systems in the department (sun.cs.tamu.edu, interactive.cs.tamu.edu, compute.cs.tamu.edu, etc.), the program is installed in /usr/local/bin/lisp. (Do not run your jobs on unix.cs.tamu.edu.)
   • See the Read-only Board for a brief example.

Topics to be covered:

See the Weekly Schedule section for more details.

1. Introduction : 1 week
2. LISP : 1 week
3. Search : 1.5 weeks
4. Game playing, alpha-beta pruning: 0.75 week
5. Propositional Logic, first-order logic, theorem proving: 3.5 weeks
6. Uncertainty, probabilistic approaches: 1.5 weeks
7. Learning: 2 weeks
8. Special topics : 1 week

Grading:

1. Exams: 45% (midterm: 20%, final: 25%)
2. Homeworks: 15% (about 3, 5% each)
3. Programming Assignments: 36% (about 3, 12% each)
4. Paper commentary: 4% (1 page, single-spaced)

Grading will be on the absolute scale. The cutoff for an `A' will be 90% of total score, 80% for a `B', 70% for a `C', 60% for a `D', and below 60% for an 'F'.

Academic Policy:

The TAMU student rules (http://student-rules.tamu.edu/), Part I Rule 20 will be strictly enforced. Local Course Policy:

• All work should be done individually and on your own unless otherwise allowed by the instructor.
• Discussion is only allowed immediately before, during, or immediately after the class, or during the instructor's office hours.
• If you find solutions to homeworks or programming assignments on the web (or in a book, etc.), you may (or may not) use it. Please check with the instructor.

Students with Disabilities:

Americans With Disabilities Act (ADA) Policy Statement: The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact the Department of Student Life: Services for Students with Disabilities in Room 126 of the Koldus Building, or call 845-1637.

1. LISP quick reference
2. CMU Common Lisp (This one will be used in the class.)
3. GCL manual (very in-depth and technical).
4. GNU Common Lisp
5. Lisp resources
6. My general resources page
7. 625/689 Reading List
8. An interesting popular view of AI

• Lecture notes (in PDF format): all notes will be uploaded in this directory.
• It is your responsibility to download, print, and bring the notes to the class. Notes will be available 24 hours before each class.
• See the 2004 Fall TAMU Calendar for breaks, etc. December 9 (Tue) is the last class day.
• When reading the chapters, you do not have to memorize everything. A separate list of terms you need to know will be handed out prior to each exam.
• All reading material below refers to the AIMA book 2nd edition. The (old XX) tags next in the Reading field are the corresponding chapters in the old AIMA book (1st edition). To see how the 1st and the 2nd edition chapters correspond, see the "AIMA 1st and 2nd edition chapter map".
• More detail will be available as we go along.

IV. Credits

Many ideas and example codes were borrowed from Gordon Novak's AI Course and Risto Miikkulainen's AI Course at the University of Texas at Austin (Course number CS381K).