CH751 퍼지시스템 특강 Uncertainties in Intelligent Systems 2004년도 제 1학기

강의진 소개 담당 교수 조성배(공대 C515;  2123-2720; sbcho@cs.yonsei.ac.kr) 웹 페이지 : http://sclab.yonsei.ac.kr/Courses/04FuSys 강의 시간 화 6, 7, 목 7 (C520) 면담 시간 월 8, 9, 수 9 담당 조교 황금성

Uncertainties in Intelligent Systems Dealing with uncertain and imprecise information has been one of the major issues in almost all intelligent system Decision making systems, diagnostic systems, intelligent agent systems, planning systems, data mining, etc Various approaches to cope with uncertain, imprecise, vague, and even inconsistent information Bayesian and probabilistic methods, belief networks, softcomputing, etc Softcomputing Neural networks, fuzzy theory, approximate reasoning, derivative-free optimization methods (GA), etc Synergy allows SC to incorporate human knowledge effectively, deal with imprecision and uncertainty, and learn to adapt to unknown or changing environments for better performance  intelligent systems to mimic human intelligence in thinking, learning, reasoning, etc

수업 교재 Textbook N. Kasabov, Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, MIT Press, 1996 References D.B. Fogel and C.J. Robinson, Computational Intelligence, Wiley Inter-Science, 2003 J. Yen and R. Langari, Fuzzy Logic: Intelligence, Control and Information, Prentice Hall, 1999 D. Ruan, Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algorithms, Kluwer Academic Publishers, 1997 I. Graham and P. L. Jones, Chapman and Hall Computing, 1988

Evaluation Criteria Evaluation Criteria Term Project (written report & oral presentation) : 60% Preliminary proposal 10% Final proposal 10% Presentation 10% Final report 30% Written Exams : 20% Homework (programming (10) + reports (2*5)) : 20% Term Project (Oral presentation is required) : Theoretical Issue (analysis, experiment, simulation) : Originality Interesting Programming (Game, Demo, etc) : Performance Survey : Completeness

Course Schedule 3/2, 3/4 : 과목소개 및 SC/AI/KE 개요 3/9, 3/11 : Rule-based systems, expert systems, fuzzy systems 3/16, 3/18 : Knowledge representation 3/23, 3/25 : Uncertainties in knowledge-based systems 3/30, 4/1 : Bayesian Network 특강 4/6, 4/8 : 1차 프로그래밍 과제 4/13, 4/15 : Machine learning methods for knowledge engineering 4/20 : 중간시험 4/27, 4/29 : 프로젝트 제안서 발표 5/4, 5/6 : Fuzzy sets and fuzzy logic 5/11, 5/13 : Fuzzy systems 5/18, 5/20 : Fuzzy system applications 5/25, 5/27 : Introduction to neural networks 6/1, 6/3 : Hybrid systems 6/8, 6/10 : 프로젝트 결과 발표 6/15 : 기말시험