Hiroshima University Syllabus

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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,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)
 
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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