KA233001 Frontier of Informatics and Data Science

Hiroshima University Syllabus

Japanese

Academic Year 2024Year 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

Instructor
(Katakana) ムカイダニ　ヒロアキ

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) 　

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

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） 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 0th April 9th (Tuesday): Course registration guidance
★1st and 2nd April 9th (Tuesday): Professor Azuma (Kyoto University)
Lecture title: Control of multi-agent systems: Control technology in the IoT era
Lecture format: Face-to-face lecture
★3rd and 4th April 16th (Tuesday): Tsubaki, Director (Institute of Statistical Mathematics)
Lecture title: (tentative) Data science as a science of solution formation process
Lecture format: Online lecture
★5th and 6th April 23rd (Tuesday): Professor Jianjun Zhao (Kyushu University)
Lecture title: The forefront of machine learning engineering research
Lecture format: Face-to-face lecture
★7th and 8th May 7th (Tuesday): Associate Professor Imaizumi (University of Tokyo)
Lecture title: Theory of deep learning
Lecture format: Online/on-demand lecture
★9th and 10th May 14th (Tuesday): Professor Ogata (Kyoto University)
Lecture title: The forefront of learning analytics research
Lecture format: Face-to-face lecture
★11th and 12th May 21st (Tuesday): Professor Moriguchi (Waseda University)
Lecture title: (tentative) Actual use of data in marketing
Lecture format: Online lecture
★13th and 14th May 28th (Tuesday): Professor Dobashi (Hokkaido University)
Lecture title: The forefront of CG research
Lecture format: Online lecture
★15th June 4th (Tue) (tentative) 10:30-12:00 (90 minutes): Kitsuregawa, Director (Inter-University Research Institute Corporation Research Organization of Information and Systems(ROIS))
Lecture title: Big data and AI
Lecture format: Online lecture

You will be asked to submit a report 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. 　

(More Details)

Learning techniques to be incorporated 　

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	2024Year	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
Instructor (Katakana)	ムカイダニ　ヒロアキ
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)
In this course, the face-to-face and online styles are mixed up.
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）	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	0th April 9th (Tuesday): Course registration guidance ★1st and 2nd April 9th (Tuesday): Professor Azuma (Kyoto University) Lecture title: Control of multi-agent systems: Control technology in the IoT era Lecture format: Face-to-face lecture ★3rd and 4th April 16th (Tuesday): Tsubaki, Director (Institute of Statistical Mathematics) Lecture title: (tentative) Data science as a science of solution formation process Lecture format: Online lecture ★5th and 6th April 23rd (Tuesday): Professor Jianjun Zhao (Kyushu University) Lecture title: The forefront of machine learning engineering research Lecture format: Face-to-face lecture ★7th and 8th May 7th (Tuesday): Associate Professor Imaizumi (University of Tokyo) Lecture title: Theory of deep learning Lecture format: Online/on-demand lecture ★9th and 10th May 14th (Tuesday): Professor Ogata (Kyoto University) Lecture title: The forefront of learning analytics research Lecture format: Face-to-face lecture ★11th and 12th May 21st (Tuesday): Professor Moriguchi (Waseda University) Lecture title: (tentative) Actual use of data in marketing Lecture format: Online lecture ★13th and 14th May 28th (Tuesday): Professor Dobashi (Hokkaido University) Lecture title: The forefront of CG research Lecture format: Online lecture ★15th June 4th (Tue) (tentative) 10:30-12:00 (90 minutes): Kitsuregawa, Director (Inter-University Research Institute Corporation Research Organization of Information and Systems(ROIS)) Lecture title: Big data and AI Lecture format: Online lecture You will be asked to submit a report 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.
(More Details)
Learning techniques to be incorporated
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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