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	Fall
Course type	Fall	Course code	1060607
Instructor(s)
近藤 敏之   [KONDO Toshiyuki]
Facility affiliation	Faculty of Engineering	Office		Email address

Course description

- The aim of the course is to understand computational intelligence methodlogy for designing intelligent machines that have adaptability.

Expected Learning

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

Required Text(s) and Materials

References

Assessment/Grading

- Attendance: 20% - Assignments (programming and/or survey of research papers): 80%

Message from instructor(s)

Course keywords

Office hours

Remarks 1

Remarks 2

Related URL

This course uses Moodle

Lecture Language

Japanese

Language Subject

Last update

3/7/2017 2:20:11 PM