Hiroshima University Syllabus

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Japanese
Academic Year 2022Year School/Graduate School School of Informatics and Data Science
Lecture Code KA222001 Subject Classification Specialized Education
Subject Name 生物統計
Subject Name
(Katakana)
セイブツトウケイ
Subject Name in
English
Biostatistics
Instructor MUKAIDANI HIROAKI,FUKUI KEISUKE,HIGAKI TORU,IMAI KATSUNOBU,ITOU YASUAKI
Instructor
(Katakana)
ムカイダニ ヒロアキ,フクイ ケイスケ,ヒガキ トオル,イマイ カツノブ,イトウ ヤスアキ
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
 
Criterion referenced
Evaluation
Informatics and Data Science Program
(Comprehensive Abilities)
・D3. Ability to examine social needs and issues which are interlinked in a complex manner, using a top-down view to solve the problems through quantitative and logical thinking based on data, diverse perspectives, and advanced skills in information processing and analysis.
 
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 Lesson 1: Guidance
Lesson 2: Big data and AI, by Dr. Kitsuregawa (Directer of National Institute of Informatics)
Lessons 3 & 4: Data science as a forming process of solutions, by Dr. Tsubaki (Directer of Institute of statistical Mathematics)
Lessons 5 & 6: Randomness viewed from computing, by Prof. Watanabe (Vice President of Tokyo Institute of Technology)
Lessons 7 & 8: Practice of data utilization in marketing, by Prof. Moriguchi (Waseda University)
Lessons 9 & 10: Open up the future of musical experience by music information processing, by Dr. Goto (National Institute of Advanced Industrial Science and Technology)
Lessons 11 & 12: Frontier in learning analytics research, by Prof. Ogata (Kyoto University)
Lessons 13 & 14: Theory of deep learning, by Prof. Imaizumi (Tokyo University)
Lessons 15 & 16: Introduction of sparse modeling, by Prof. Nagahara (Kitakyushu City University)

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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