| １． |
Outline |
|
We overview artificial intelligence and discuss its limitations and future. Also, this class deals with the following topics; history of AI, classical AI, reinforcement learning, deep leaning, expert system, neural network, Bayes' theorem, symbol grounding problem, frame problem, etc.
|
| ２． |
Objectives |
|
The aim of the course is to learn fundamental methods and techniques of artificial intelligence.
|
| ３． |
Grading Policy |
|
Students are evaluated with mini-reports in each lecture (30%), a mid-term exam (30%) and a term exam (40%).
|
| ４． |
Textbook and Reference |
|
A Japanese book (ISBN978-4-7973-7026-3) is used.
Following textbooks written in English are recommended.
-Stuart Russel, Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition, Pearson Education Limited, ISBN978-1292153964, 2016.
-Rolf Pfeifer, Christian Scheier, Understanding Intelligence, ISBN978-0262661256, 2001.
|
| ５． |
Requirements (Assignments) |
|
Students need to use LMS.
|
| ６． |
Note |
|
In this class, students must think, discuss and solve problems in Japanese rather than passively listen to lecture.
|
| ７． |
Schedule |
|
| １. Introduction: what is artificial intelligence (AI)? |
| ２. History of AI: the birth of AI (Dartmouth Conference), good old-fashioned AI, AI winter, AI boom, technological singularity, etc. |
| ３. Machine learning, reinforcement learning, genetic algorithm, deep learning, etc. |
| ４. Expert system, board game AI, narrow AI, etc. |
| ５. Neural networks |
６. Data mining, search algorithm, Bayes' theorem, etc.
|
| ７. Game theory, game AI, minimax, etc. |
| ８. Summary, mid-term exam |
| ９. Subsumption architecture, fuzzy logic, etc. |
| 10. Natural language processing, machine translation, conversational agent, etc. |
| 11. Decision making algorithm, artificial life, etc. |
| 12. Symbol grounding problem |
| 13. Frame problem |
| 14. Future of AI |
| 15. Summary, final exam |
|
| １． |
Outline |
|
We overview artificial intelligence and discuss its limitations and future. Also, this class deals with the following topics; history of AI, classical AI, reinforcement learning, deep leaning, expert system, neural network, Bayes' theorem, symbol grounding problem, frame problem, etc.
|
| ２． |
Objectives |
|
The aim of the course is to learn fundamental methods and techniques of artificial intelligence.
|
| ３． |
Grading Policy |
|
Students are evaluated with mini-reports in each lecture (30%), a mid-term exam (30%) and a term exam (40%).
|
| ４． |
Textbook and Reference |
|
A Japanese book (ISBN978-4-7973-7026-3) is used.
Following textbooks written in English are recommended.
-Stuart Russel, Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition, Pearson Education Limited, ISBN978-1292153964, 2016.
-Rolf Pfeifer, Christian Scheier, Understanding Intelligence, ISBN978-0262661256, 2001.
|
| ５． |
Requirements (Assignments) |
|
Students need to use LMS.
|
| ６． |
Note |
|
In this class, students must think, discuss and solve problems in Japanese rather than passively listen to lecture.
|
| ７． |
Schedule |
|
| １. Introduction: what is artificial intelligence (AI)? |
| ２. History of AI: the birth of AI (Dartmouth Conference), good old-fashioned AI, AI winter, AI boom, technological singularity, etc. |
| ３. Machine learning, reinforcement learning, genetic algorithm, deep learning, etc. |
| ４. Expert system, board game AI, narrow AI, etc. |
| ５. Neural networks |
６. Data mining, search algorithm, Bayes' theorem, etc.
|
| ７. Game theory, game AI, minimax, etc. |
| ８. Summary, mid-term exam |
| ９. Subsumption architecture, fuzzy logic, etc. |
| 10. Natural language processing, machine translation, conversational agent, etc. |
| 11. Decision making algorithm, artificial life, etc. |
| 12. Symbol grounding problem |
| 13. Frame problem |
| 14. Future of AI |
| 15. Summary, final exam |
|
|