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CSI8751 Topics in AI Machine Learning: Methodologies and Applications

CSI8751 Topics in AI Machine Learning: Methodologies and Applications. Fall Semester, 2010. Human. EC. Soft Computing. NN. FL. EC. PC. Game. Bioinformatics. MNN. Social Agent. Evolvable HW. Robot. PCR HWR. CBR, FD, AD. Backgrounds. HMM. FCN. BN. Speciation. SASOM. BM, MR.

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CSI8751 Topics in AI Machine Learning: Methodologies and Applications

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  1. CSI8751 Topics in AIMachine Learning:Methodologies and Applications Fall Semester, 2010

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

  3. 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 • Tue7, 8 • Teaching assistant • Lee, Young-Seol

  4. 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

  5. Textbook • 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

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

  7. 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

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

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