ANM21001 Topics in Mathematical and Information Sciences I

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Integrated Arts and Sciences Department of Integrated Arts and Sciences

Lecture Code ANM21001 Subject Classification Specialized Education

Subject Name 情報数理学特講Ｉ

Subject Name
（Katakana）ジョウホウスウリガクトッコウI

Subject Name in
English Topics in Mathematical and Information Sciences I

Instructor See the "Class Schedule" of the syllabus

Instructor
(Katakana) シラバスジュギョウケイカクトウサンショウ

Campus Higashi-Hiroshima Semester/Term 3rd-Year,  Second Semester,  Intensive

Days, Periods, and Classrooms (Int) Inte：IAS C808

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

Lecture style may change to online depending on traffic and weather conditions.
Lectures (using blackboard and screen), Discussions,                 Exercises (presentations on issues)

Credits 2.0 Class Hours/Week 　 Language of Instruction J : Japanese

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 01 : Mathematics/Statistics

Eligible Students

Keywords Mathematical modeling, complex systems, nonlinear mathematics, programming

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) Integrated Arts and Sciences
（Knowledge and Understanding）
・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework.
（Abilities and Skills）
・The ability and skills to specify necessary theories and methods for consideration of issues.

Class Objectives
/Class Outline In the first half of the lecture, the methods of mathematical modeling of natural, biological, and social phenomena are explained from the viewpoints of both stochastic processes and deterministic processes. In the second half of the lecture, the techniques of mathematical modeling will be explained based on concrete examples, and then simple problems related to mathematical modeling will be presented.
In addition, to deepen students' understanding of modeling, sample python programs will be distributed for them to run on their own.

Class Schedule Lesson 1: Introduction ~ What is Mathematical Model?
Lesson 2: Modeling of  Stochastic Process I
Lesson 3: Modeling of Stochastic Process II
Lesson 4: Modeling of Deterministic Process I
Lesson 5: Modeling of Deterministic Process II
Lesson 6: Case Study I: Modeling Social Phenomena 1
Lesson 7: Case Study II: Modeling Social Phenomena 2
Lesson 8: Exercises in Modeling Social Phenomena
Lesson 9: Case Study III: Modeling of Natural Phenomena 1　　　
Lesson 10: Case Study IV: Modeling of Natural Phenomena 2
Lesson 11: Exercises in  Modeling Natural Phenomena
Lesson 12: Case Study V: Modeling of Life Phenomena 1
Lesson 13: Case Study VI: Modeling of Life Phenomena 2
Lesson 14: Exercises in Modeling Life Phenomena
Lesson 15: Overall summary and exercises

Basically, evaluation is based on reports. Presentations and participation in discussions in lectures are also taken into consideration.

Text/Reference
Books,etc. Materials will be basically distributed.
References (Japanese)
1. Masayasu Mimura et al., Introduction to Phenomenological Mathematics, University of Tokyo Press, 2013.
2. Mizuki Oka et al, "ALife in the Making and Running - Introduction to Artificial Life Model Theory through Implementation" , O'Reilly Japan, 2018

PC or AV used in
Class,etc. Handouts

(More Details) Handouts and PowerPoint presentation.

Learning techniques to be incorporated

Suggestions on
Preparation and
Review Preparation: Read the materials distributed in advance. It is preferable to be able to run the distributed python programs on your own PC.
Review: summarize important points in a notebook.

Requirements

Grading Method Evaluated by reports and presentations in lectures.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other Lecture schedule will be posted as soon as the schedule is finalized.

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 Integrated Arts and Sciences Department of Integrated Arts and Sciences
Lecture Code	ANM21001	Subject Classification	Specialized Education
Subject Name	情報数理学特講Ｉ
Subject Name （Katakana）	ジョウホウスウリガクトッコウI
Subject Name in English	Topics in Mathematical and Information Sciences I
Instructor	See the "Class Schedule" of the syllabus
Instructor (Katakana)	シラバスジュギョウケイカクトウサンショウ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, Second Semester, Intensive
Days, Periods, and Classrooms	(Int) Inte：IAS C808
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
Lecture style may change to online depending on traffic and weather conditions. Lectures (using blackboard and screen), Discussions, Exercises (presentations on issues)
Credits	2.0	Class Hours/Week		Language of Instruction	J : Japanese
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	01 : Mathematics/Statistics
Eligible Students
Keywords	Mathematical modeling, complex systems, nonlinear mathematics, programming
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)	Integrated Arts and Sciences （Knowledge and Understanding）・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework. （Abilities and Skills）・The ability and skills to specify necessary theories and methods for consideration of issues.
Class Objectives /Class Outline	In the first half of the lecture, the methods of mathematical modeling of natural, biological, and social phenomena are explained from the viewpoints of both stochastic processes and deterministic processes. In the second half of the lecture, the techniques of mathematical modeling will be explained based on concrete examples, and then simple problems related to mathematical modeling will be presented. In addition, to deepen students' understanding of modeling, sample python programs will be distributed for them to run on their own.
Class Schedule	Lesson 1: Introduction ~ What is Mathematical Model? Lesson 2: Modeling of Stochastic Process I Lesson 3: Modeling of Stochastic Process II Lesson 4: Modeling of Deterministic Process I Lesson 5: Modeling of Deterministic Process II Lesson 6: Case Study I: Modeling Social Phenomena 1 Lesson 7: Case Study II: Modeling Social Phenomena 2 Lesson 8: Exercises in Modeling Social Phenomena Lesson 9: Case Study III: Modeling of Natural Phenomena 1　　　 Lesson 10: Case Study IV: Modeling of Natural Phenomena 2 Lesson 11: Exercises in Modeling Natural Phenomena Lesson 12: Case Study V: Modeling of Life Phenomena 1 Lesson 13: Case Study VI: Modeling of Life Phenomena 2 Lesson 14: Exercises in Modeling Life Phenomena Lesson 15: Overall summary and exercises Basically, evaluation is based on reports. Presentations and participation in discussions in lectures are also taken into consideration.
Text/Reference Books,etc.	Materials will be basically distributed. References (Japanese) 1. Masayasu Mimura et al., Introduction to Phenomenological Mathematics, University of Tokyo Press, 2013. 2. Mizuki Oka et al, "ALife in the Making and Running - Introduction to Artificial Life Model Theory through Implementation" , O'Reilly Japan, 2018
PC or AV used in Class,etc.	Handouts
(More Details)	Handouts and PowerPoint presentation.
Learning techniques to be incorporated
Suggestions on Preparation and Review	Preparation: Read the materials distributed in advance. It is preferable to be able to run the distributed python programs on your own PC. Review: summarize important points in a notebook.
Requirements
Grading Method	Evaluated by reports and presentations in lectures.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	Lecture schedule will be posted as soon as the schedule is finalized.
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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