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(2019)

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Intelligent Systems

 YAMANE, Ken
  Elective  2 credits
【 Informatics Science〈Correspondence Course〉(Master's Degree Program)･full year】
19-3-1724-3192

１．
Outline
We overview classical artificial intelligence and discuss its limitations. Also, this course deals with the following topics: soft-computing, pattern recognition and machine learning.
２．
Objectives
The aim of the course is to learn fundamental concepts and techniques of intelligent systems.
３．
Grading Policy
Evaluated with reports (75%) and a term exam (25%).
４．
Textbook and Reference
No textbook is used.

The following book written in English is recommended.
-Stuart Russel, Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition, Pearson Education Limited, ISBN978-1292153964, 2016.
５．
Requirements (Assignments)
Students should use E-mail and LMS.
６．
Note
Basic skills of programming and the knowledge of computer science are required for students.
７．
Schedule
１. Introduction
２. Classical artificial intelligent I
３. Classical artificial intelligent II
４. Classical artificial intelligent III
５. Limitations of AI
６. Subsumption architecture
７. Soft-computing I
８. Soft-computing II
９. Soft-computing III
10. Soft-computing IV
11. Pattern recognition and machine learning I
12. Pattern recognition and machine learning II
13. Reinforcement learning I
14. Reinforcement learning II
15. Summary
１．
Outline
We overview classical artificial intelligence and discuss its limitations. Also, this course deals with the following topics: soft-computing, pattern recognition and machine learning.
２．
Objectives
The aim of the course is to learn fundamental concepts and techniques of intelligent systems.
３．
Grading Policy
Evaluated with reports (75%) and a term exam (25%).
４．
Textbook and Reference
No textbook is used.

The following book written in English is recommended.
-Stuart Russel, Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition, Pearson Education Limited, ISBN978-1292153964, 2016.
５．
Requirements (Assignments)
Students should use E-mail and LMS.
６．
Note
Basic skills of programming and the knowledge of computer science are required for students.
７．
Schedule
１. Introduction
２. Classical artificial intelligent I
３. Classical artificial intelligent II
４. Classical artificial intelligent III
５. Limitations of AI
６. Subsumption architecture
７. Soft-computing I
８. Soft-computing II
９. Soft-computing III
10. Soft-computing IV
11. Pattern recognition and machine learning I
12. Pattern recognition and machine learning II
13. Reinforcement learning I
14. Reinforcement learning II
15. Summary