OLD WEBPAGE for
CPSC 420-200 Artificial Intelligence (Honors):
Spring 2004

Syllabus

NEWS: 5/11/04, 11:20PM (Tue)
  • [5/11] Final grades grades (click here): includes program #3, paper commentary, and final exam scores (you need username/password).
Read-Only Bulletin Board.: 2/4/04, 05:20PM (Wed)

Page last modified: 4/9/04, 01:32PM Friday.

General Information Resources Weekly Schedule Credits Lecture Notes

I. General Information

Instructor:

Dr. Yoonsuck Choe
Email: choe(a)tamu.edu
Office: HRBB 322B
Phone: 845-5466
Office hours: MWF 11:10-12:10pm

TA:

Heejin Lim
Email: hjlim(a)cs.tamu.edu
Office: HRBB 322A
Phone: 845-5481
Office hours: MWF 3-4pm

Prerequisite/Restrictions:

CPSC 311

Lectures:

MWF 9:10am-10am HRBB 126.

Goals:

To understand the problems in AI and to learn how to solve them:
  1. traditional AI techniques (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

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. You can choose your own language to use for the assignments, but you have to first get permission from the instructor.
  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 : 0.75 week
  5. Propositional Logic, First-order logic: 3.5 weeks
  6. Uncertainty : 1 weeks
  7. Learning : 2.5 weeks
  8. Special Topics : 1 week

Grading:

  1. Exams: 45% (midterm: 20%, final: 25%)
  2. Homeworks (about 3): 15%
  3. Programming Assignments (about 3): 36%
  4. Paper commentary (about 1): 4%
Grading will be on the absolute scale. The cutoff for an `A' will be at most 90% of total score, 80% for a `B', 70% for a `C', and 60% for a `D'. However, these cutoffs might be lowered at the end of the semester to accomodate the actual distribution of grades.

Academic Policy:

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

Local course policy is as follows:

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 Room 126 of the Koldus Building, or call 845-1637. (The source of this passage is TAMU Phil320 Syllabus.)

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. 420 Reading List

III. Weekly Schedule and Class Notes

Week
Date
Topic
Reading
Assignments
Notices and Dues
Notes
1 1/19 MLK Day (Holiday)      
1 1/21 Introduction Chapter 1
1.1 and 1.2
    slide01.pdf
1 1/23 Introduction Chapter 26
26.1 and 26.2
    slide01.pdf
2 1/26 Lisp Lisp quick ref Program #1 (see slide02); skeleton code (deriv.lsp)   slide02.pdf
2 1/28 No class     (Make-up 2/2 6-8pm HRBB302) slide02.pdf
2 1/30 No class     (Make-up 2/2 6-8pm HRBB302) slide02.pdf
3 2/2 Uninformed Search
(BFS,DFS,DLS,IDS)
Chapter 3.1-3.5
(3.6,3.7 optional)
  Make-up class today 6-8pm HRBB302 slide03.pdf
3 2/4 Informed
Search (BFS,Greedy,A*)
Chapter 4.1-4.3 (4.4 optional)
(old 4.1-4.3)
    slide03.pdf
3 2/6       slide03.pdf
4 2/9 IDA*,Heuristic Search,
Simulated Annealing, etc.
Chapter 4     slide03.pdf
4 2/11 Game playing
Min-Max, Alpha-Beta
Chapter 5 (optional) and 6.1-6.8 (old 5)     slide04.pdf
4 2/13       slide04.pdf
5 2/16 Game playing wrap up
Propositional Logic
Chapter 7.1, 7.3, 7.5, 7.6 (old 6) Program #2 Assigned (see slide04) Program #1 due (in class) slide04.pdf
slide05.pdf
5 2/18 Theorem proving Chapter 9 (old 10)     slide05.pdf
5 2/20 No class     Business trip.
6 2/23 Theorem proving Chapter 9 (old 10)     slide05.pdf
6 2/25 First-order logic Chapter 8 (old 7)     slide06.pdf
6 2/27   Homework #1 (due 3/3)   slide06.pdf
7 3/1     Program #2 due extended to 3/12;
Midterm review - 3/2 (Tue) 6pm HRBB 302 (slides [pdf])
slide06.pdf
7 3/3 Inference
for FOL
Chapter 9   Homework #1 due slide06.pdf
7 3/5 Midterm Exam    
8 3/8 Theorem proving
for FOL
Chapter 9 (old 10)   HW1/Prog1/Midterm Grades announced slide06.pdf
8 3/10 Uncertainty Chapter 13 (old 14)     slide07.pdf
8 3/12   Program #3 TBA Program #2 due slide07.pdf
9 3/15 Spring Break      
9 3/17 Spring Break      
9 3/19 Spring Break      
10 3/22 Uncertainty;
Probablistic
reasonong
"; Chapter 14 (old 15)     slide07.pdf
10 3/24 Inductive Learning Chapter 18 Program #3 announced   slide08.pdf
10 3/26       slide08.pdf
11 3/29 Learning (supervised) Chapter 20 (old 19)     slide08.pdf
11 3/31       slide08.pdf
11 4/2     Q-drop slide08.pdf
12 4/5       slide08.pdf
12 4/7 Unsupervised learning       slide08.pdf
12 4/9 Reading Day     No class
13 4/12 Unsupervised learning       slide08.pdf
13 4/14 Evolutionary learning   Homework #2 [pdf] announced   slide08.pdf
13 4/16 Semantics in autonomuos agents Choe & Bhamidipati (2003)     slide09.pdf
14 4/19 Semantics in autonomuos agents Choe & Bhamidipati (2003)     slide09.pdf
14 4/21 Analogy Choe (2002)   Program #3 Due (by midnight) slide10.pdf
14 4/23 Analogy   Program #3 Due (by 9 slide10.pdf

15 4/26 Natural language processing Chapter 22 (old 22)     slide11.pdf
15 4/28       slide11.pdf
15 4/30     Homework 2 due slide11.pdf
16 5/3 Distributed Representation Binary Spatter Code     slide12.pdf
165/4Final Review  
 5/10FinalExam 8:00-10:00am
Paper commentary due
 

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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