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Syllabus

Infomation of Course

Program Bachelor [학사과정] Course Type Major Elective [ 전공선택 ]
Course Code 31.414 Course No IE414
Section English English
L:L:C(AU) 3:1:3.0(0) Exam time
(classroom)
Tue: 13:00~15:45
()
Course Title Cognitive Science and Systems [ 인지과학과 시스템 ]
Class time
(classroom)
Tue: 13:00~14:30 / (E2)Industrial Engineering & Management Bldg. [ (E2)산업경영학동 ] (1125)
Thu: 13:00~14:30 / (E2)Industrial Engineering & Management Bldg. [ (E2)산업경영학동 ] (1125)
Notice

Information of Professor

Name 윤완철(Yoon, Wan Chul)
Department 산업및시스템공학과(Department of Industrial & Systems Engineering)
Phone 042-350-3119
E-Mail wanyoon@kaist.ac.kr

Plan of Lecture

Syllabus File
Syllabus URL
Summary of Lecture Introduction to human cognition in knowledge-based works including topics such as human memory, inference, planning, decision-making, and learning. Also introduced in parallel are artificial intelligence methods for problem solving and machine learning that include rule-based reasoning, automated planning, solution space search, genetic algorithms, and artificial neural networks. The final project is to design a human-computer joint cognitive system in which the two agents cooperate to solve complex problems in real world.
Material for Teaching - Main textbook : Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving (6th Ed.), 2009
- Auxiliary textbook : Wikipedia and Internet for Human Cognition Topics
Evaluation Criteria * The followings evaluation criteria may change:
A. Attendance: 10 % B. Midterm exam: 25 % C. Final exam: 25 %
E. Report: 20 % G. Project: 20 %
* Limitations on course retaking, if any:
Not recommended for students who have taken CS470 or similar courses.
Lecture Schedule Week Subject
------------------------------------------------
1 Overview of Cognitive Science and AI
2 Human Problem Solving and Search Methods
3 GPS and CSP
4 Search by Genetic Algorithms
5 Learning Classifier System
6 Logical Inference and Rule-based Reasoning
7 Automated Planning
8 (Midterm)
9 Human Memory and Knowledge Structures
10 Managing Knowledge and Works
11 Knowledge Representation (Sematic nets
12 Artificial Neural Network Models
13 Human Learning Strategies
14 Machine Learning I
15 Machine Learning II
16 (Final Exam)
Memo Assistant Name: Kwon, Hyuk Tae