| Teachers | YAMANE, Ken | |
|---|---|---|
| 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 |
|---|---|---|---|
| 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 |
