Artificial Intelligence (A.I.) has many definitions according to researchers, but it can be said as a study for analyzing
human intelligence and implementing it on computers.
Since all topics related to A.I. cannot be expressed because A.I. has close relations with various fields and consists of a wide variety of contents, several basic technologies and algorithms will be treated through pre-learning subjects, post-learning subjects, lectures, and discussions.
Since all topics related to A.I. cannot be expressed because A.I. has close relations with various fields and consists of a wide variety of contents, several basic technologies and algorithms will be treated through pre-learning subjects, post-learning subjects, lectures, and discussions.
In this class, students will learn about several basic technical fields and algorithms in A.I. and will think about basic
questions such as what is human intelligence, how we confirm whether intelligence is realized on computers or not.
The aim of this class is to establish students' views of A.I. by learning and thinking about A.I. through pre-learning subjects, post-learning subjects, lectures, and discussions.
The aim of this class is to establish students' views of A.I. by learning and thinking about A.I. through pre-learning subjects, post-learning subjects, lectures, and discussions.
- Students can understand the basics of technical knowledge on A.I. and can explain them to others.
- Students can express ones' opinion on the impacts of A.I. on our society.
- Students can acquire practical knowledge on a survey in various fields and can apply it to a surveys in the future.
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1. | Overview of the class and a guidance of Library. | Reading syllabus carefully and preparing an answer to the pre-lecture subject. | 180minutes |
| Answering the post-lecture subject. | 80minutes | ||
| 2. | A.I. and its history 1. Discussion on answers for pre-lecture subject, 2. Explaining brief history of A.I. |
Answering the pre-lecture subject. | 180minutes |
| 3. | Three big-waves of research on A.I. 1. The first wave: ELIZA and SHRDLU 2. The second wave: Knowledge engineering and Japanese Fifth-Generation Computer Project. 3. THe third wave: the new trends. |
Answering the pre-lecture subject. | 180minutes |
| 4. | Knowledge Representation and Knowledge Acquisition. Experiments on Knowledge Acquisition. |
Answering the pre-lecture subject. | 180minutes |
| Answering Post-lecture subject. | 180minutes | ||
| 5. | Inference Basics of Symbolic Logic and its application to Knowledge Representation. |
Answering pre-lecture subject. | 180minutes |
| 6. | Implicit Knowledge and the Frame Problem. | Answering pre-lecture subject. | 180minutes |
| 7. | Mid-term Examination and its explanation | Preparation of carry-on paper and mid-term examination. | 300minutes |
| 8. | Search tree and searching strategies | Answering pre-lecture subject. | 180minutes |
| 9. | Machine Learning | Answering pre-lecture subject. | 180minutes |
| 10. | Games and A.I. | Answering pre-lecture subject. | 180minutes |
| Answering post-lecture subject. | 180minutes | ||
| 11. | Expert Systems | Answering pre-lecture subject. | 240minutes |
| 12. | Natural Language Processing and Image Processing | Answering pre-lecture subject. | 240minutes |
| 13. | Various problems caused by possibilities of A.I. | Answering pre-lecture subject. | 240minutes |
| Answering post-lecture subject. | 240minutes | ||
| 14. | Final Examination and its explanation. | Preparation of carry-on paper and mid-term examination. | 360minutes |
| Total. | - | - | 3680minutes |
| Pre- and Post lecture subjects | Mid-term Examination | Final examinaation | Total. | |
|---|---|---|---|---|
| 1. | 10% | 10% | 20% | 40% |
| 2. | 10% | 10% | 15% | 35% |
| 3. | 10% | 10% | 5% | 25% |
| Total. | 30% | 30% | 40% | - |
The weighted average of Pre- and Post-lecture subjects, Mid-term exam, and Final exam.
Students who can understand problems in subjects and Exams will be graded as 60%.
Students who can understand problems in subjects and Exams will be graded as 60%.
Taking classes of "Discrete Mathematics", and "Data structure and Algorithms" will be strongly recommended.
- Course that cultivates an ability for utilizing knowledge
| Work experience | Work experience and relevance to the course content if applicatable |
|---|---|
| N/A | N/A |
- 4.QUALITY EDUCATION
- 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Sat Mar 21 12:36:13 JST 2020