322751 Paradigms of Artificial Intelligence
Rationale
Artificial intelligence (AI) is a branch of computer science that is concerned
with the automation of intelligent behavior.
This course provides a
comprehensive exposure to the paradigms and techniques necessary for study
and research in artificial intelligence. Emphasis is placed on intelligent
problem-solving strategies and emerging trends in technology.
Catalog Description
AI Languages. Problem Representation. Search Strategies. Knowledge
Representation and Reasoning. Machine Learning Techniques: support vector
machines, Bayesian belief network, fuzzy logic, artificial neural network,
genetic algorithm.
Credit:
Term:
Lecture time:
Venue:
3(3-0-6)
2/2012
Sunday: 09.00–12.00 am.
SC8405
Course Outline:
I.
Overview of Artificial intelligence
a. Definition
b. History and applications
c. Future Trend
(3 hours)
II.
Problem-solving as search
a. State space search
b. Uninformed search
c. Informed search
(6 hours)
III.
Artificial Intelligence Languages
a. LISP programming in search
b. PROLOG programming in search
(9 hours)
IV.
Knowledge Representation & reasoning
(6 hours)
a. Representation structures (semantic, frames, scripts,…)
b. Expert systems, uncertainty
V.
Machine learning: support vector machines
(6 hours)
a. SVM theory and fundamentals
b. Kernel functions
c. MATLAB programming in SVM
VI. Machine learning: Bayesian belief networks
(6 hours)
a. BBN theory and fundamentals
b. MSBNx software for BBN
VII. Machine learning: genetic algorithm
(3 hours)
VIII. Machine learning: fuzzy logic
(3 hours)
a. Theory and Fundamentals
b. MATLAB programming in fuzzy logic
IX. Selected AI topics
(6 hours)
a. cellular automata
i. research and applications at CAKE lab
CS@KKU
b. bio-inspired computational algorithms and applications
http://www.intechopen.com/search?q=artificial+intelligence
i. term projects
Textbook(s):
1. Lecture notes
2. Boonserm Kijsirikul. Artificial Intelligence (Thai), Department of
Computer Engineering, Chulalongkorn University.
3. J. Giarratano, G. Riley, Expert Systems, 4th edition, 2005. Thomson
Learning, Inc.
4. G. Luger, Artificial Intelligence: structures and strategies for complex
problem solving: 4th edition, 1997. Addison Wesley.
5. Elaine Rich, Kevin Knight, Artificial Intelligence, 2nd edition, 1991.
McGraw-Hill, Inc.
6. Check eBooks:
http://www.intechopen.com/search?q=artificial+intelligence
Grading System :
Final grade will be computed according to the following weight
distribution:
Midterm exam
35 %
Final exam
35 %
Assignments & mini-projects
30 %
Closed book examinations are usually given both in midterm and finals.
Instructor :
Sartra Wongthanavasu, Ph.D.
Associate professor of Computer Science, CS@KKU
Email: sartrawong@me.com
Office:: SC6320
TA :
Jetsada Ponkaew, Doctoral student
Email: j_android@hotmail.com
Office: Doctoral student room
Website: http://csaikku.wordpress.com