| Course title | |||||
| 論理回路 [Logic Circuit] | |||||
| Course category | Requirement | Credit | 2 | ||
| Department | Year | ～ | Semester | Fall | |
| Course type | Fall | Course code | 106t0035 | ||
| Instructor(s) | |||||
| 吉田 誠, 小瀬 亮太, 長田 容, 金野 尚武 [YOSHIDA Makoto, KOSE Ryota, OSADA You, KONNO Naotake] | |||||
| Facility affiliation | Faculty of Agriculture | Office | Email address | ||
| Course description |
|
Class Code: lsgucmp - The purpose of the course is to understand computational intelligence methodology for designing intelligent machines that have adaptability, such as artificial neural networks, evolutionary computation, reinforcement learning, etc. |
| Expected Learning |
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The standard of this lecture is to understand computational intelligence methodologies such as artificial neural networks, evolutionary computation, reinforcement learning. See the Curriculum maps. |
| Course schedule |
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1. Introduction - Good Old Fashioned Artificial Intelligence (GOFAI) and Artificial Life 2. Neural Computation (1) - Adaptive Mechanism of Primitive Creature - Perceptron 3. Neural Computation (2) - Supervised learning of Neural Networks (Back Propagation method) 4. Neural Computation (3) - Hopfield networks model and Boltzmann Machine 5. Evolutionary Computation (1) - Introduction of Genetic Algorithms (GA) 6. Evolutionary Computation (2) - Real-coded GA and Multi-objective GA 7. Evolutionary Computation (3) - Other Evolutionary Computational Approach: EA, EP, GP, PSO, ACO 8. Reinforcement Learning (1) - Principle of Optimality, Markov Decision Process, State Value function 9. Reinforcement Learning (2) - Action Value function, Q-Learning, TD-Learning, Actor-Critic method 10. Reinforcement Learning (3) - Recent topics in RL 11. Unsupervised Learning (1) - Clustering 12. Unsupervised Learning (2) - Deep Learning 13. Design principle for Intelligent Machine (1) - Cognitive Psychology 14. Design principle for Intelligent Machine (2) - Brain Science, Huma Motor Learning 15. Design principle for Intelligent Machine (3) - BMI/BCI, Human-Robot interaction |
| Prerequisites |
| In addition to 30 hours that students spend in the class, students are recommended to prepare for and revise the lectures, spending the standard amount of time as specified by the University. |
| Required Text(s) and Materials |
| Digital textbooks are distributed in the class. |
| References |
| Assessment/Grading |
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Based on the score of assignments (programming and/or survey of research papers). The grade evaluation in this online class is premised on all attendances, and comprehensively evaluates the attitude to learn, quizzes, report, and online tests. Standard study time set by the our university is required to get the grade. |
| Message from instructor(s) |
| I believe that computational Intelligence that learns from living systems would be quite an interesting topic for the students in Computer Science. |
| Course keywords |
| Machine Learning, Neural Networks, Evolutionary Computation, Reinforcement Learning |
| Office hours |
| Ask questions at any time by email. |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| This course uses Google classroom. |
| Lecture Language |
| Japanese |
| Language Subject |
| Last update |
| 3/6/2023 1:31:07 PM |