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1.교수목표 및 강의개요 (Course Objectives &
Description)
1) 교수목표
- To understand the basic concept of problem solving by searching and apply
it to practical problems - To cultivate the ability to formalize real-world
problems and solve them by using logical or mathematical reasoning methods
- To study various learning algorithms and apply them to real-world
problems – To understand current applications of AI
2) 강의개요
The course introduces students the methods of search, knowledge
representation, inference and the theories of learning as computational
models useful in the implementation of intelligent systems. This course
will contain basic concept and some applications of uncertain reasoning.
The application system using AI techniques such as robotics, expert
systems, and natural language processing will also be introduced.
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장애학생의 경우
장애학습지원센터와 강의 및 과제에 대한 사전 협의가 가능합니다.
2.주교재 (Required TextBook)
Artificial Intelligence: A Modern Approach, 3rd ed. Stuart Russell and
Peter Norvig, Prentice Hall, 2009
3.평가방법 (Requirements & Grading)
- Mid.Exam:40% - Final.Exam:40% - Assignment:20%
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장애학생의 경우
시험시간의 연장이 가능하며, 대필이나 컴퓨터를 활용하여 시험에 응할 수 있습니다.
4.주별
강의계획 (Schedule)
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주 별
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강의 및 실험실기내용
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과제 및 기타 참고사항
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제1주
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[표절 등 학술적 부정행위 예방교육실시] Introduction,
Solving Problems by Searching
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제2주
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[표절 등 학술적 부정행위 예방교육실시] Informed Search
and Exploration
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Homework #1: Search Application and Implementation
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제3주
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Constraint Satisfaction Problems
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제4주
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Logical Agents
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제5주
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Reasoning & Knowledge Representation
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제6주
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Uncertainty
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Homework #2: Exercise
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제7주
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Uncertainty (continued)
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제8주
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Bayesian Networks, Midterm Examination
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Homework #3: Exercise
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제9주
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Inference in Bayesian Networks
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제10주
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Bayesian and Prior Knowledge
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제11주
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C4.5 and Rule Learning
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Homework #4: Exercise
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제12주
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Learning from Observations
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제13주
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Learning from Observations (continued)
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Homework #5: Implementation of Learning Application
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제14주
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Statistical Learning Methods
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제15주
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Final Examination
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제16주
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5.참고문헌 (References)
Related videos(EBS documentary etc.)
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