KA241001 Neural Networks

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Informatics and Data Science

Lecture Code KA241001 Subject Classification Specialized Education

Subject Name 神経回路網

Subject Name
（Katakana）シンケイカイロモウ

Subject Name in
English Neural Networks

Instructor FUKUSHIMA MAKOTO

Instructor
(Katakana) フクシマ　マコト

Campus Higashi-Hiroshima Semester/Term 3rd-Year, First Semester, 1Term

Days, Periods, and Classrooms (1T) Weds5-8：ECON B155

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Lectures and Exercises

Credits 2.0 Class Hours/Week 4 Language of Instruction B : Japanese／English

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords Artificial Neural Networks (ANNs), Biological Neural Networks (BNNs)

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) This course is positioned as a neural network course with an emphasis on classroom lectures and manual calculations.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic.

Data Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Intelligence Science Program
（Knowledge and Understanding）
・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers.
（Abilities and Skills）
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Class Objectives
/Class Outline In this course, students learn about artificial neural networks (ANNs) in Lessons 1-8, biological neural networks (BNNs) in Lessons 9-14, and a special type of ANNs and its fusion with BNNs in Lesson 15. The goal is to understand the fundamentals of ANN-based machine learning methods, as well as BNN models and analysis methods.

Class Schedule Lesson 1: Class Orientation/Deep Neural Networks 1
Lesson 2: Deep Neural Networks 2
Lesson 3: Gradient Descent
Lesson 4: Backpropagation
Lesson 5: Regularization 1
Lesson 6: Regularization 2
Lesson 7: Convolutional Networks 1
Lesson 8: Convolutional Networks 2
Lesson 9: Models of Neurons 1
Lesson 10: Models of Neurons 2
Lesson 11: Brain Network Analysis: Basics 1
Lesson 12: Brain Network Analysis: Basics 2
Lesson 13: Brain Network Analysis: Advanced 1
Lesson 14: Brain Network Analysis: Advanced 2
Lesson 15: Reservoir Computing

Assignments in Lessons 4, 6, 8, 10, 12, and 14

Text/Reference
Books,etc. [References]
Christopher M. Bishop, Hugh Bishop. Deep Learning: Foundations and Concepts. Springer, 2023. (Lessons 1-8)
Alex Fornito, Andrew Zalesky, Edward T. Bullmore. Fundamentals of Brain Network Analysis. Academic Press, 2016. (Lessons 11-14)

PC or AV used in
Class,etc. Microsoft Teams

(More Details) Lecture materials will be made available through Microsoft Teams.

Learning techniques to be incorporated

Suggestions on
Preparation and
Review Use the lecture materials for preparation and review.

Requirements The instructor assumes that students have already learned the basics of calculus, linear algebra, and probability theory. Students are required to thoroughly review the contents of Lessons 1-8 of the Machine Learning course in the third term of the second year.

Grading Method Evaluation is based on the grades of the submitted assignments.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message Some of the lessons may be delivered online or on-demand. The order of Lesson 15 may be moved up. In either case, students will be notified in advance.

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.

Academic Year	2025Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA241001	Subject Classification	Specialized Education
Subject Name	神経回路網
Subject Name （Katakana）	シンケイカイロモウ
Subject Name in English	Neural Networks
Instructor	FUKUSHIMA MAKOTO
Instructor (Katakana)	フクシマ　マコト
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 1Term
Days, Periods, and Classrooms	(1T) Weds5-8：ECON B155
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
Lectures and Exercises
Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords	Artificial Neural Networks (ANNs), Biological Neural Networks (BNNs)
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	This course is positioned as a neural network course with an emphasis on classroom lectures and manual calculations.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic. Data Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Intelligence Science Program （Knowledge and Understanding）・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers. （Abilities and Skills）・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
Class Objectives /Class Outline	In this course, students learn about artificial neural networks (ANNs) in Lessons 1-8, biological neural networks (BNNs) in Lessons 9-14, and a special type of ANNs and its fusion with BNNs in Lesson 15. The goal is to understand the fundamentals of ANN-based machine learning methods, as well as BNN models and analysis methods.
Class Schedule	Lesson 1: Class Orientation/Deep Neural Networks 1 Lesson 2: Deep Neural Networks 2 Lesson 3: Gradient Descent Lesson 4: Backpropagation Lesson 5: Regularization 1 Lesson 6: Regularization 2 Lesson 7: Convolutional Networks 1 Lesson 8: Convolutional Networks 2 Lesson 9: Models of Neurons 1 Lesson 10: Models of Neurons 2 Lesson 11: Brain Network Analysis: Basics 1 Lesson 12: Brain Network Analysis: Basics 2 Lesson 13: Brain Network Analysis: Advanced 1 Lesson 14: Brain Network Analysis: Advanced 2 Lesson 15: Reservoir Computing Assignments in Lessons 4, 6, 8, 10, 12, and 14
Text/Reference Books,etc.	[References] Christopher M. Bishop, Hugh Bishop. Deep Learning: Foundations and Concepts. Springer, 2023. (Lessons 1-8) Alex Fornito, Andrew Zalesky, Edward T. Bullmore. Fundamentals of Brain Network Analysis. Academic Press, 2016. (Lessons 11-14)
PC or AV used in Class,etc.	Microsoft Teams
(More Details)	Lecture materials will be made available through Microsoft Teams.
Learning techniques to be incorporated
Suggestions on Preparation and Review	Use the lecture materials for preparation and review.
Requirements	The instructor assumes that students have already learned the basics of calculus, linear algebra, and probability theory. Students are required to thoroughly review the contents of Lessons 1-8 of the Machine Learning course in the third term of the second year.
Grading Method	Evaluation is based on the grades of the submitted assignments.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	Some of the lessons may be delivered online or on-demand. The order of Lesson 15 may be moved up. In either case, students will be notified in advance.
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.

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