Course title
知能機械デザイン学特論   [Intelligent Machine Design]
Course category courses for master's programs  Requirement   Credit 2 
Department   Year   Semester 1st 
Course type 1st  Course code 1060607
Instructor(s)
近藤 敏之   [KONDO Toshiyuki]
Facility affiliation Faculty of Engineering Office   Email address

Course description
Class Code: zjukhyu

- 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
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
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
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
3/28/2022 10:07:50 AM