CSI8751 Topics in AIMachine Learning:

Methodologies and Applications

Fall Semester, 2010

Backgrounds

NN

MNN

FL EC

PC

Soft Computing EC

Human

PCRHWR

IDS

HMM

TC, Web Mining

FCN

BM, MR

Conversational Agent

SASOMSVM

BN Speciation

Bioinformatics

Robot

CBR, FD, AD

Social Agent

Evolvable HW

Game

Teaching Staff

Professor

– Cho, Sung-Bae (Eng. C515; 2123-2720; sbcho@cs.yonsei.ac.kr)

Course webpage: http://sclab.yonsei.ac.kr/courses/10TAI

Class hours

– Tue 5, Thu 5, 6 (Eng. A019)

Office hours

– Tue 7, 8

Teaching assistant

– Lee, Young-Seol

Course Objectives

Understanding machine learning technologies such as decision tree, artificial neural networks, genetic algorithms, etc

Developing systems to solve complex real-world problems effectively by applying them

Textbook

– T.M. Mitchell, Machine Learning, McGraw Hill, 1997

References

– T. Dean, J. Allen and Y. Aloimonos, Artificial Intelligence: Theory and Practice, The Benjamin/Cummings Pub., 1995

– S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 1995

– P.H. Winston, Artificial Intelligence, 3rd Ed, Addison Wesley, 1992

– P. Baldi, Bioinformatics: The Machine Learning Approach, MIT Press, 2001

Textbook

Course Schedule

1. 9/2 : Course overview2. 9/7, 9/9 : Introduction (Mitchell, Ch1)3. 9/14, 9/16 : Concept Learning (Mitchell, Ch2)4. 9/21, 9/23 : HW#1 (Chu-Seok)5. 9/28, 9/30 : Decision Tree Learning (Mitchell, Ch3)6. 10/5, 10/7 : Artificial Neural Networks (Mitchell, Ch4)7. 10/12, 10/14 : Evaluating Hypothesis (Mitchell, Ch5)8. 10/19, 10/21 : Term-paper proposal9. 10/26, 10/28 : Bayesian Learning (Mitchell, Ch6)10. 11/2, 11/4 : HW#211. 11/9, 11/11 : Computational Learning Theory (Mitchell, Ch7)12. 11/16, 11/18 : Instance-based Learning (Mitchell, Ch8)13. 11/23, 11/25 : Genetic Algorithms (Mitchell, Ch9)14. 11/30, 12/2 : Final Exam15. 12/7, 12/9 : Final presentation16. 12/14, 12/16 : Due date for term-paper

Evaluation Criteria

Evaluation Criteria

– Term Project (written report and an oral presentation) : 40%

– Final Exam : 20%

– Homeworks : 20%

– Presentation & Participaption : 20%

Term Project (Oral presentation is required) :

– Theoretical Issue (Analysis, Experiment, Simulation) : Originality

– Interesting Programming (Game, Demo, etc) : Performance

– Survey : Completeness

List of Possible Projects

Tangible Agent Integrated Model Life Browser Bayesian Network for Middleware Cluster GA SASOM for Motion Recognition Evolvability Evolutionary Neural Networks