CSI8751 Topics in AI Machine Learning: Methodologies and Applications
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Transcript of CSI8751 Topics in AI Machine Learning: Methodologies and Applications
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; [email protected])
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