Teachers | YAMANE, Ken | |
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Grade, Semester | Year 1 full year [Division of Informatics Science〈Correspondence Course〉(Master's Degree Program)] | |
Category | Special Subjects | |
Classes | メディア授業 | |
Elective, Credits | Elective 2credit | |
Syllabus Number |
We overview classical artificial intelligence and discuss its limitations. Also, this course deals with the following topics: soft-computing, pattern recognition and machine learning.
The aim of the course is to learn fundamental concepts and techniques of intelligent systems.
Evaluated with reports (75%) and a term exam (25%).
Kind | Title | Author | Publisher |
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Textbook | 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. | ||
References |
Basic skills of programming and the knowledge of computer science are required for students.
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 |