KA233001 Frontier of Informatics and Data Science

Hiroshima University Syllabus

Japanese

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

Lecture Code KA233001 Subject Classification Specialized Education

Subject Name 情報科学の最前線

Subject Name
（Katakana）ジョウホウカガクノサイゼンセン

Subject Name in
English Frontier of Informatics and Data Science

Instructor MUKAIDANI HIROAKI,ADILIN ANUARDI,KAMEI SAYAKA,RAYTCHEV BISSER ROUMENOV

Instructor
(Katakana) ムカイダニ　ヒロアキ,アディリン　アヌアルディ,カメイ　サヤカ,ライチェフ　ビセル　ルメノフ

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

Days, Periods, and Classrooms (1T) Tues1-4：ENG 107

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive)

In this course, the face-to-face and online styles are mixed up.

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

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students Third Year Students

Keywords computer science, data science, intelligence science, frontier in informatics and data sceince

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) Computer Science Program
（Knowledge and Understanding）
・C1. Knowledge and ability to work on problem-solving after understanding that various issues existing in human beings, society, and individuals can be interpreted in multiple ways depending on social conditions and culture.

Data Science Program
（Knowledge and Understanding）
・C1. Knowledge and ability to work on problem-solving after understanding that various issues existing in human beings, society, and individuals can be interpreted in multiple ways depending on social conditions and culture.

Intelligence Science Program
（Knowledge and Understanding）
・C1. Knowledge and ability to work on problem-solving after understanding that various issues existing in human beings, society, and individuals can be interpreted in multiple ways depending on social conditions and culture.

Class Objectives
/Class Outline 生物統計/Biostatistics is replaced by ``Frontier in Informatics and Data Science'' from 2022.

In this course, we invite the top-level researchers in Japan, and summarize the most recent theory and technologies in computer science, data science and intelligence science. More specifically, this is a omnibus course by eights distinguished researchers,
and aims at introducing the research progress in each research topic.

Class Schedule ★1st and 2nd lectures, Tuesday, April 9th: Guidance
★3rd and 4th lectures, Tuesday, April 15th: Director Hironori Tsubaki (Institute of Statistical Mathematics)
Lecture title: Data science as the science of the solution formation process
Lecture format: Online lecture
★5th and 6th lectures, Tuesday, April 22nd: Professor Jianjun Zhao (Kyushu University)
Lecture title: The cutting edge of machine learning engineering research
Lecture format: In-person lecture
★7th and 8th lectures, Thursday, May 1st: Professor Hiroaki Ogata (Kyoto University)
Lecture title: The cutting edge of learning analytics research
Lecture format: In-person lecture
★9th and 10th lectures, Tuesday, May 13th: Professor Yoshinori Dobashi (Hokkaido University)
Lecture title: The cutting edge of CG research
Lecture format: In-person lecture
★11th and 12th lectures, Tuesday, May 20th: Associate Professor Masaaki Imaizumi (University of Tokyo)
Lecture title: Theory of deep learning
Lecture format: On-demand lecture
★13th and 14th lectures, Tuesday, May 27th: Professor Tsuyoshi Moriguchi (Waseda University)
Lecture title: Actual data utilization in marketing
Lecture format: Online lecture
★15th and 16th lectures, Tuesday, June 3rd, Professor Toru Namekawa (Keio University)
Lecture title: New developments in control theory that tackle social issues
Lecture format: Face-to-face lecture
Reports will be submitted after each lecture.

Students are requested to submit the report for each lecture by the due date.

Text/Reference
Books,etc. No text book will not be used.

PC or AV used in
Class,etc. Handouts, Microsoft Teams, moodle

(More Details)

Learning techniques to be incorporated Discussions

Suggestions on
Preparation and
Review No preparation for the lecture is needed. However, since the handouts may not be provided in the lecture, students should take notes to complete the reports.

Requirements

Grading Method All reports have to be submitted by the due date.

Practical Experience Experienced

Summary of Practical Experience and Class Contents based on it This lecture is managed by top-level researchers and practitioners and aims at introducing the on-going research progress of respective fields in computer science, data science and and intelligence science.

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.

Academic Year	2025Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA233001	Subject Classification	Specialized Education
Subject Name	情報科学の最前線
Subject Name （Katakana）	ジョウホウカガクノサイゼンセン
Subject Name in English	Frontier of Informatics and Data Science
Instructor	MUKAIDANI HIROAKI,ADILIN ANUARDI,KAMEI SAYAKA,RAYTCHEV BISSER ROUMENOV
Instructor (Katakana)	ムカイダニ　ヒロアキ,アディリン　アヌアルディ,カメイ　サヤカ,ライチェフ　ビセル　ルメノフ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 1Term
Days, Periods, and Classrooms	(1T) Tues1-4：ENG 107
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)
In this course, the face-to-face and online styles are mixed up.
Credits	2.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students	Third Year Students
Keywords	computer science, data science, intelligence science, frontier in informatics and data sceince
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)	Computer Science Program （Knowledge and Understanding）・C1. Knowledge and ability to work on problem-solving after understanding that various issues existing in human beings, society, and individuals can be interpreted in multiple ways depending on social conditions and culture. Data Science Program （Knowledge and Understanding）・C1. Knowledge and ability to work on problem-solving after understanding that various issues existing in human beings, society, and individuals can be interpreted in multiple ways depending on social conditions and culture. Intelligence Science Program （Knowledge and Understanding）・C1. Knowledge and ability to work on problem-solving after understanding that various issues existing in human beings, society, and individuals can be interpreted in multiple ways depending on social conditions and culture.
Class Objectives /Class Outline	生物統計/Biostatistics is replaced by ``Frontier in Informatics and Data Science'' from 2022. In this course, we invite the top-level researchers in Japan, and summarize the most recent theory and technologies in computer science, data science and intelligence science. More specifically, this is a omnibus course by eights distinguished researchers, and aims at introducing the research progress in each research topic.
Class Schedule	★1st and 2nd lectures, Tuesday, April 9th: Guidance ★3rd and 4th lectures, Tuesday, April 15th: Director Hironori Tsubaki (Institute of Statistical Mathematics) Lecture title: Data science as the science of the solution formation process Lecture format: Online lecture ★5th and 6th lectures, Tuesday, April 22nd: Professor Jianjun Zhao (Kyushu University) Lecture title: The cutting edge of machine learning engineering research Lecture format: In-person lecture ★7th and 8th lectures, Thursday, May 1st: Professor Hiroaki Ogata (Kyoto University) Lecture title: The cutting edge of learning analytics research Lecture format: In-person lecture ★9th and 10th lectures, Tuesday, May 13th: Professor Yoshinori Dobashi (Hokkaido University) Lecture title: The cutting edge of CG research Lecture format: In-person lecture ★11th and 12th lectures, Tuesday, May 20th: Associate Professor Masaaki Imaizumi (University of Tokyo) Lecture title: Theory of deep learning Lecture format: On-demand lecture ★13th and 14th lectures, Tuesday, May 27th: Professor Tsuyoshi Moriguchi (Waseda University) Lecture title: Actual data utilization in marketing Lecture format: Online lecture ★15th and 16th lectures, Tuesday, June 3rd, Professor Toru Namekawa (Keio University) Lecture title: New developments in control theory that tackle social issues Lecture format: Face-to-face lecture Reports will be submitted after each lecture. Students are requested to submit the report for each lecture by the due date.
Text/Reference Books,etc.	No text book will not be used.
PC or AV used in Class,etc.	Handouts, Microsoft Teams, moodle
(More Details)
Learning techniques to be incorporated	Discussions
Suggestions on Preparation and Review	No preparation for the lecture is needed. However, since the handouts may not be provided in the lecture, students should take notes to complete the reports.
Requirements
Grading Method	All reports have to be submitted by the due date.
Practical Experience	Experienced
Summary of Practical Experience and Class Contents based on it	This lecture is managed by top-level researchers and practitioners and aims at introducing the on-going research progress of respective fields in computer science, data science and and intelligence science.
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.

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