WSG21901 Advanced Learning Systems

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Electrical, Systems, and Control Engineering Program

Lecture Code WSG21901 Subject Classification Specialized Education

Subject Name 学習システム特論

Subject Name
（Katakana）ガクシュウシステムトクロン

Subject Name in
English Advanced Learning Systems

Instructor HAYASHIDA TOMOHIRO

Instructor
(Katakana) ハヤシダ　トモヒロ

Campus Higashi-Hiroshima Semester/Term 1st-Year, Second Semester, Intensive

Days, Periods, and Classrooms (Int) Inte

Lesson Style Lecture Lesson Style
(More Details) 　

Lecture and exercise.

Credits 2.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 5 : Graduate Basic

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 11 : Electrical, Systems, and Control Engineering

Eligible Students Master's program students of Graduate school of Engineering

Keywords Neural networks, convolutional neural network, deep learnig, Python

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 You will learn the basics of neural network and deep learning which are important concepts in artificial intelligence, agent-based simulation, robotics, optimal control and other fields. Also understand implementation techniques through programming exercises.

Class Schedule lesson1 guidance
lesson2 perceptron
lesson3 neural network
lesson4 neural network
lesson5 learning process
lesson6 learning process
lesson7 back propagation
lesson8 back propagation
lesson9 techniques for efficient learning
lesson10 techniques for efficient learning
lesson11 Programming
lesson12 convolutional neural networks
lesson13 convolutional neural networks
lesson14 Deep learning
lesson15 Programming

Text/Reference
Books,etc. Handout will be uploaded

PC or AV used in
Class,etc. 　

(More Details) PC (powerpoint, PDF)

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review The handouts will be uploaded on the website during class, so please use it for review.

Requirements

Grading Method You will be evaluated according to the submitted reports and exercises.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other In the lecture, Japanese is used as the basic language, and some technical terms are introduced in English.

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.

Academic Year	2024Year	School/Graduate School	Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Electrical, Systems, and Control Engineering Program
Lecture Code	WSG21901	Subject Classification	Specialized Education
Subject Name	学習システム特論
Subject Name （Katakana）	ガクシュウシステムトクロン
Subject Name in English	Advanced Learning Systems
Instructor	HAYASHIDA TOMOHIRO
Instructor (Katakana)	ハヤシダ　トモヒロ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, Second Semester, Intensive
Days, Periods, and Classrooms	(Int) Inte
Lesson Style	Lecture	Lesson Style (More Details)
Lecture and exercise.
Credits	2.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	5 : Graduate Basic
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	11 : Electrical, Systems, and Control Engineering
Eligible Students	Master's program students of Graduate school of Engineering
Keywords	Neural networks, convolutional neural network, deep learnig, Python
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	You will learn the basics of neural network and deep learning which are important concepts in artificial intelligence, agent-based simulation, robotics, optimal control and other fields. Also understand implementation techniques through programming exercises.
Class Schedule	lesson1 guidance lesson2 perceptron lesson3 neural network lesson4 neural network lesson5 learning process lesson6 learning process lesson7 back propagation lesson8 back propagation lesson9 techniques for efficient learning lesson10 techniques for efficient learning lesson11 Programming lesson12 convolutional neural networks lesson13 convolutional neural networks lesson14 Deep learning lesson15 Programming
Text/Reference Books,etc.	Handout will be uploaded
PC or AV used in Class,etc.
(More Details)	PC (powerpoint, PDF)
Learning techniques to be incorporated
Suggestions on Preparation and Review	The handouts will be uploaded on the website during class, so please use it for review.
Requirements
Grading Method	You will be evaluated according to the submitted reports and exercises.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	In the lecture, Japanese is used as the basic language, and some technical terms are introduced in English.
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.

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