CPSC 625-600 Artificial Intelligence:
Fall 2007

Syllabus

NEWS: 12/10/07, 04:42PM (Mon)
Read-Only Bulletin Board.: 8/31/04, 12:02PM (Tue)

Page last modified: 9/5/07, 12:21PM Wednesday.

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

I. General Information

Instructor:

Dr. Yoonsuck Choe
Email: choe(a)tamu.edu
Office: HRBB 322B
Phone: 979-845-5466
Office hours: MWF 1:40pm–2:40pm.

TA:

Huei-Fang Yang
Email: hfyang(a)cs.tamu.edu
Office: HRBB 322A
Phone: 979-845-5481
Office hours: T 4:30pm–6pm, Th 1:00pm–2:30pm

Prerequisite/Restrictions:

CPSC 311; If you already took undergrad-level AI, do not take this course, unless the content is significantly different from what you took.

Lectures:

MWF 12:40pm–1:30am HRBB 126

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.
  2. CMU Common Lisp:

Topics to be covered:

See the Weekly Schedule section for more details.
  1. Introduction
  2. LISP
  3. Search
  4. Game playing, alpha-beta pruning
  5. Propositional Logic, first-order logic, theorem proving
  6. Uncertainty, probabilistic approaches
  7. Learning
  8. Planning
  9. Special topics

Grading:

  1. Exams: 35% (midterm: 15%, final: 20%)
  2. Homeworks: 15% (about 3, 5% each)
  3. Programming Assignments: 24% (about 2, 12% each)
  4. Term project: 20%
  5. Paper commentary: 6% (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
  9. Chess playing program (with neat visualization)

III. Weekly Schedule and Class Notes

Week
Date
Topic
Reading
Assignments
Notices and Dues
Notes
1 8/27 Introduction Chapter 1
1.1 and 1.2
  First day of class slide01.pdf
1 8/29 Introduction Chapter 1
1.1 and 1.2
    slide01.pdf
1 8/31 Lisp Lisp quick ref     slide02.pdf
2 9/3 Lisp, Symbolic Differentiation Lisp quick ref     slide02.pdf
2 9/5 Uninformed Search (BFS,DFS,DLS,IDS) Chapter 3.1-3.5 (3.6,3.7 optional) Program 1 announced   slide03.pdf
2 9/7 Uninformed Search (BFS,DFS,DLS,IDS) Chapter 3.1-3.5 (3.6,3.7 optional)     slide03.pdf
3 9/10 Informed Search (BestFS,Greedy,A*) Chapter 4.1-4.3 (4.4 optional)(old 4.1-4.3)     slide03.pdf
3 9/12 IDA*,Heuristic Search,
Simulated Annealing, etc.
Chapter 4     slide03.pdf
3 9/14 IDA*,Heuristic Search,
Simulated Annealing, etc.
Chapter 4   Program 1 due (11:59pm) slide03.pdf
4 9/17 Game playing
Min-Max, Alpha-Beta
Chapter 5 (optional) and 6.1-6.8 (old 5)     slide03.pdf
4 9/19 Game playing
Chapter 5 (optional) and 6.1-6.8 (old 5) Program 2 announced   slide03.pdf
4 9/21 Game playing
Chapter 5 (optional) and 6.1-6.8 (old 5)     slide03.pdf
5 9/24 Game playing wrap up; Propositional Logic Chapter 7.1, 7.3, 7.5, 7.6 (old 6)     slide03.pdf
slide04.pdf
5 9/26 Theorem proving Chapter 9 (old 10) Homework 1 TBA   slide04.pdf
5 9/28 FOL; Theorem proving
for FOL
Chapter 8 (old 7); Chapter 9 (old 10)   slide04.pdf
6 10/1 FOL; Theorem proving
for FOL
Chapter 8 (old 7); Chapter 9 (old 10)   slide04.pdf
6 10/3 Inference
for FOL
Chapter 9   Program 2 due slide04.pdf
6 10/5 Inference
for FOL
Chapter 9   Program 2 due slide04.pdf
7 10/8 Uncertainty Chapter 13 (old 14)   Homework 1 due slide05.pdf
7 10/10 Midterm Exam        
7 10/12 Uncertainty Chapter 13 (old 14)     slide05.pdf
8 10/15 Uncertainty Chapter 13 (old 14), Chapter 14 (old 15)     slide05.pdf
8 10/17 Uncertainty Chapter 13 (old 14), Chapter 14 (old 15)     slide05.pdf
8 10/19 Uncertainty Chapter 13 (old 14), Chapter 14 (old 15)     slide05.pdf
9 10/22 Uncertainty Chapter 13 (old 14), Chapter 14 (old 15)     slide05.pdf
9 10/24 Learning Chapter 14 (old 15)     slide06.pdf
9 10/26 Learning Chapter 18   slide06.pdf
10 10/29 Learning Chapter 18     slide06.pdf
10 10/31 Learning Chapter 20 (old 19)     slide06.pdf
10 11/2 Learning Chapter 20 (old 19)     slide06.pdf
11 11/5 No class     Make up TBA  
11 11/7 Final exam        
11 11/9 Learning       slide06.pdf
12 11/12 Learning       slide06.pdf
12 11/14 Learning       slide06.pdf
12 11/16 Learning       slide06.pdf
13 11/19 Advanced topics Autonomous semantics     slide07.pdf
13 11/21 Advanced topics Autonomous semantics     slide07.pdf
13 11/23 Thanksgiving: No class        
14 11/26 Advanced topics Thalamus and analogy      
14 11/28 Project presentation        
14 11/30 Project presentation        
15 12/3 Project presentation        

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).

$Id: index.php,v 1.4.1.9 2007/08/27 04:51:09 choe Exp choe $