| Academic Year |
2025Year |
School/Graduate School |
Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program |
| Lecture Code |
WSN30401 |
Subject Classification |
Specialized Education |
| Subject Name |
情報科学特別講義D |
Subject Name
（Katakana） |
ジョウホウカガクトクベツコウギディー |
Subject Name in
English |
Special Lecture on Informatics and Data Science D |
| Instructor |
See the class timetable. |
Instructor
(Katakana) |
ジュギョウジカンワリヲサンショウ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, First Semester, Intensive |
| Days, Periods, and Classrooms |
(Int) Inte |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
| Lecture |
| Credits |
1.0 |
Class Hours/Week |
|
Language of Instruction |
B
:
Japanese／English |
| Course Level |
7
:
Graduate Special Studies
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
Kalman filter, reinforcement learning |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
The theme of this lecture is state estimation and control of discrete-time systems. The objective is to understand the Kalman filter as a method of state estimation for discrete-time systems, and reinforcement learning as a method of control for discrete-time systems. |
| Class Schedule |
1st Lecture: Basics of probability statistics and recursive least squares method
2nd Lecture: Linear Kalman filter
3rd Lecture: Extended Kalman filter and unscented Kalman filter
4th Lecture: Particle filter
5th Lecture: Markov decision process
6th Lecture: Estimation of value function
7th Lecture: Policy gradient method
8th Lecture: Deep reinforcement learning
We will evaluate from report |
Text/Reference
Books,etc. |
A report assignment will be given in the final session. There will be no exam. |
PC or AV used in
Class,etc. |
Handouts |
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
The knowledge required to understand this lecture is calculus and linear algebra, so it is recommended that those who are unsure of their comprehension review the material in advance. |
| Requirements |
|
| Grading Method |
Evaluation will be based on reports. |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |