CSI 8751 Topics in AI Machine Learning Methodologies

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CSI 8751 Topics in AI Machine Learning: Methodologies and Applications Fall Semester, 2010

CSI 8751 Topics in AI Machine Learning: Methodologies and Applications Fall Semester, 2010

Backgrounds Human EC Soft Computing PC FCN Bioinformatics FL BM, MR NN EC SASOM

Backgrounds Human EC Soft Computing PC FCN Bioinformatics FL BM, MR NN EC SASOM MNN SVM Game Social Agent Evolvable HW Conversational Agent TC, Web Mining IDS HMM BN Speciation Robot PCR HWR CBR, FD, AD

Teaching Staff 4 Professor – Cho, Sung-Bae (Eng. C 515; 2123 -2720; sbcho@cs. yonsei.

Teaching Staff 4 Professor – Cho, Sung-Bae (Eng. C 515; 2123 -2720; sbcho@cs. yonsei. ac. kr) 4 Course webpage: http: //sclab. yonsei. ac. kr/courses/10 TAI 4 Class hours – Tue 5, Thu 5, 6 (Eng. A 019) 4 Office hours – Tue 7, 8 4 Teaching assistant – Lee, Young-Seol

Course Objectives 4 Understanding machine learning technologies such as decision tree, artificial neural networks,

Course Objectives 4 Understanding machine learning technologies such as decision tree, artificial neural networks, genetic algorithms, etc 4 Developing systems to solve complex real-world problems effectively by applying them

Textbook 4 Textbook – T. M. Mitchell, Machine Learning, Mc. Graw Hill, 1997 4

Textbook 4 Textbook – T. M. Mitchell, Machine Learning, Mc. Graw Hill, 1997 4 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, 3 rd Ed, Addison Wesley, 1992 – P. Baldi, Bioinformatics: The Machine Learning Approach, MIT Press, 2001

Course Schedule 1. 9/2 : Course overview 2. 9/7, 9/9 : Introduction (Mitchell, Ch

Course Schedule 1. 9/2 : Course overview 2. 9/7, 9/9 : Introduction (Mitchell, Ch 1) 3. 9/14, 9/16 : Concept Learning (Mitchell, Ch 2) 4. 9/21, 9/23 : HW#1 (Chu-Seok) 5. 9/28, 9/30 : Decision Tree Learning (Mitchell, Ch 3) 6. 10/5, 10/7 : Artificial Neural Networks (Mitchell, Ch 4) 7. 10/12, 10/14 : Evaluating Hypothesis (Mitchell, Ch 5) 8. 10/19, 10/21 : Term-paper proposal 9. 10/26, 10/28 : Bayesian Learning (Mitchell, Ch 6) 10. 11/2, 11/4 : HW#2 11. 11/9, 11/11 : Computational Learning Theory (Mitchell, Ch 7) 12. 11/16, 11/18 : Instance-based Learning (Mitchell, Ch 8) 13. 11/23, 11/25 : Genetic Algorithms (Mitchell, Ch 9) 14. 11/30, 12/2 : Final Exam 15. 12/7, 12/9 : Final presentation 16. 12/14, 12/16 : Due date for term-paper

Evaluation Criteria 4 Evaluation Criteria – – Term Project (written report and an oral

Evaluation Criteria 4 Evaluation Criteria – – Term Project (written report and an oral presentation) Final Exam Homeworks Presentation & Participaption : 40% : 20% 4 Term Project (Oral presentation is required) : – Theoretical Issue (Analysis, Experiment, Simulation) : Originality – Interesting Programming (Game, Demo, etc) : Performance – Survey : Completeness

List of Possible Projects 4 Tangible Agent 4 Integrated Model 4 Life Browser 4

List of Possible Projects 4 Tangible Agent 4 Integrated Model 4 Life Browser 4 Bayesian Network for Middleware 4 Cluster GA 4 SASOM for Motion Recognition 4 Evolvability 4 Evolutionary Neural Networks