CPSC 420-500 Artificial Intelligence:
Spring 2006


NEWS: 5/9/06, 03:43PM (Tue)
  • [5/09] Final letter grades posted: Please see the instructor from 11am-12pm Wed (5/9) if you have questions about the grade.
  • [5/09] The final letter grades followed the absolute scale (i.e., it was not curved, as curving actually increased the cut line).
  • ----------
  • Grades grades/.
  • Skeleton codes src/.
  • Lecture notes lectures/.
  • Read-only board Read Only Board.
Read-Only Bulletin Board.: 1/17/05, 01:15PM (Mon)

Page last modified: 4/24/06, 11:36AM Monday.

General Information Resources Weekly Schedule Credits Lecture Notes Example Code Read-Only Board

I. General Information


Dr. Yoonsuck Choe
Email: choe(a)tamu.edu
Office: HRBB 322B
Phone: 845-5466
Office hours: MW 2:30pm--4:00pm.


Yifang Liu
Office: Richardson 909
Email: yfliu(a)cs.tamu.edu
Phone: 979-845-7104
Office hours: T/R 10:00-11:30am


CPSC 311


M/W 4:10pm-5:25pm, HRBB 113


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.).


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:

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


  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 Integrity Statement:

AGGIE HONOR CODE: An Aggie does not lie, cheat, or steal or tolerate those who do.

Upon accepting admission to Texas A&M University, a student immediately assumes a commitment to uphold the Honor Code, to accept responsibility for learning, and to follow the philosophy and rules of the Honor System. Students will be required to state their commitment on examinations, research papers, and other academic work. Ignorance of the rules does not exclude any member of the TAMU community from the requirements or the processes of the Honor System.

For additional information please visit: http://www.tamu.edu/aggiehonor/

Local Course Policy:

Students with Disabilities:

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 Cain Hall or call 845-1637.

II. Resources

  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

III. Weekly Schedule and Class Notes

Notices and Dues
1 1/16 MLK Day No class      
1 1/18 Introduction Chapter 1
1.1 and 1.2
  First day of class slide01.pdf
2 1/23 Introduction Chapter 26
26.1 and 26.2
  Unix basics (DIY); Last day to drop a course is 9/2 slide01.pdf
2 1/25 Lisp Lisp quick ref     slide02.pdf
3 1/30 Lisp (Symbolic Differentiation)   Prog. Asmt. #1 Announced (due 2/16)   slide02.pdf
3 2/1 Uninformed Search (BFS,DFS,DLS,IDS) Chapter 3.1-3.5 (3.6,3.7 optional)     slide03.pdf
4 2/6 No class     Project PI meeting at NIH (make-up to be announced) slide00.pdf
4 2/8 IDA*,Heuristic Search,
Simulated Annealing, etc.
Chapter 4   slide03.pdf
5 2/13 Informed Search (BestFS,Greedy,A*) Chapter 4.1-4.3 (4.4 optional)(old 4.1-4.3)     slide03.pdf
5 2/15 Game playing
Min-Max, Alpha-Beta
Chapter 5 (optional) and 6.1-6.8 (old 5)   Prog. Asmt. #1 2/16 (midnight) slide03.pdf
6 2/20 Game playing wrap up; Propositional Logic Chapter 7.1, 7.3, 7.5, 7.6 (old 6)     slide03.pdf
6 2/22 Theorem proving Chapter 9 (old 10) HW#1 announced; Prog. Asmt. #2 to be announced (Fri) Friday (2/24): Make-up class 4:10pm slide04.pdf
7 2/27 FOL; Theorem proving
for FOL
Chapter 8 (old 7); Chapter 9 (old 10)   slide04.pdf
7 3/1 Inference
for FOL
Chapter 9   HW#1 due (3/3 4pm) slide04.pdf
8 3/6 Midterm Exam 3/6: Midsemester grades due In class exam.  
8 3/8 Uncertainty Chapter 13 (old 14)     slide05.pdf
9 3/13 Spring break        
9 3/15 Spring break        
10 3/20 Uncertainty Chapter 13 (old 14)     slide05.pdf
10 3/22 Uncertainty (continuted),Learning Chapter 13 (old 14), Chapter 14 (old 15)   Program #2 due (for +5 credit) slide05.pdf
11 3/27 Learning Chapter 18   Program #2 due (for full credit) slide05.pdf
11 3/29 No class Make-up TBA   NIH meeting  
12 4/3 Learning Chapter 18   11/5 (Q-drop) slide06.pdf
12 4/5     slide06.pdf
13 4/10 Learning (Nnets) Chapter 20 (old 19) Homework #3   slide06.pdf
13 4/12 Natural language processing       slide07.pdf
14 4/17   Paper Commentary Asmt. to be announced Course evaluation slide07.pdf
14 4/19 Advanced topics Thalamus and analogy     slide08.pdf
15 4/24 Advanced topics Analogy   Homework #3 due (in class); slide09.pdf
15 4/26 Advanced topics Biologically inspired vision      
16 5/1 Topic TBA     Last day of class.
Program #3 due (5/1 Monday 11:59pm)
 5/8Final Exam  3:30--5:30pm, HRBB 113
Paper commentary due (in class)

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).

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