TB151201 Advanced Seminar on Affective Data Science

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Graduate School of Biomedical and Health Sciences (Doctoral Course)

Lecture Code TB151201 Subject Classification Specialized Education

Subject Name 感性データサイエンス特別演習

Subject Name
（Katakana）カンセイデータサイエンストクベツエンシュウ

Subject Name in
English Advanced Seminar on Affective Data Science

Instructor CHIKAZOE JUNICHI,PHAM QUANG TRUNG

Instructor
(Katakana) チカゾエ　ジュンイチ,ファム　クアン　チュン

Campus Kasumi Semester/Term 1st-Year, First Semester, First Semester

Days, Periods, and Classrooms (1st) Inte

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

Exercise-oriented, Group Discussions, Student Presentations, Hands-on Workshops

Credits 2.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 6 : Graduate Advanced

Course Area（Area） 27 : Health Sciences

Course Area（Discipline） 01 : Medical Sciences

Eligible Students

Keywords

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)

Class Objectives
/Class Outline In this seminar, students will conduct critiques of the latest academic papers related to their doctoral research themes and report on their own research progress. Through critical discussion, students will verify logical consistency, clarify core research problems, and develop the communication skills necessary to convey research findings effectively.
Regardless of the timing of enrollment or academic year, this course provides opportunities for reporting and discussion tailored to each student's progress. The goal is to acquire the essential communicative competence required of a professional researcher.

Class Schedule lesson1: Research Ethics and Annual Planning
lesson2: Literature Review 1 (In-depth reading and critical examination of major recent papers)
lesson3: Literature Review 2 (In-depth reading and critical examination of major recent papers)
lesson4: Literature Review 3 (In-depth reading and critical examination of major recent papers)
lesson5: Research Design 1 (Research plans for April enrollees / Advanced proposals for October enrollees)
lesson6: Research Design 2 (Research plans for April enrollees / Advanced proposals for October enrollees)
lesson7: Research Design 3 (Research plans for April enrollees / Advanced proposals for October enrollees)
lesson8: Methodology Review 1 (Evaluation of experimental design validity)
lesson9: Methodology Review 2 (Evaluation of experimental design validity)
lesson10: Methodology Review 3 (Evaluation of experimental design validity)
lesson11: Interpreting Results and Defining "Kansei Value"
lesson12: Mid-term Presentations 1 (Progress reports and Q&A sessions)
lesson13: Mid-term Presentations 2 (Progress reports and Q&A sessions)
lesson14: Mid-term Presentations 3 (Progress reports and Q&A sessions)
lesson15: First-term Summary (Feedback for the next term)

Text/Reference
Books,etc. The Elements of Statistical Learning

PC or AV used in
Class,etc. Text, Handouts, Audio Materials, Visual Materials

(More Details)

Learning techniques to be incorporated Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning

Suggestions on
Preparation and
Review Functional MRI (fMRI) is an extremely powerful research method that allows for the non-invasive investigation of cognitive functions in living humans. Resting-state fMRI, in particular, is known to be useful for clinical diagnosis and shows great promise for medical applications. Students are expected to learn fMRI data analysis methods, understand the specific characteristics of this data, and become capable of applying appropriate machine learning algorithms.

Requirements

Grading Method Grading will be based on the student's level of understanding regarding the nature of fMRI data and the characteristics of various machine learning algorithms.

Contribution to Class Exercises
-Content of the Final Presentation
-Contribution to Class Exercises
-Content of the Final Presentation

Practical Experience

Summary of Practical Experience and Class Contents based on it

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	2026Year	School/Graduate School	Graduate School of Biomedical and Health Sciences (Doctoral Course)
Lecture Code	TB151201	Subject Classification	Specialized Education
Subject Name	感性データサイエンス特別演習
Subject Name （Katakana）	カンセイデータサイエンストクベツエンシュウ
Subject Name in English	Advanced Seminar on Affective Data Science
Instructor	CHIKAZOE JUNICHI,PHAM QUANG TRUNG
Instructor (Katakana)	チカゾエ　ジュンイチ,ファム　クアン　チュン
Campus	Kasumi	Semester/Term	1st-Year, First Semester, First Semester
Days, Periods, and Classrooms	(1st) Inte
Lesson Style	Seminar	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)
Exercise-oriented, Group Discussions, Student Presentations, Hands-on Workshops
Credits	2.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	6 : Graduate Advanced
Course Area（Area）	27 : Health Sciences
Course Area（Discipline）	01 : Medical Sciences
Eligible Students
Keywords
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)
Class Objectives /Class Outline	In this seminar, students will conduct critiques of the latest academic papers related to their doctoral research themes and report on their own research progress. Through critical discussion, students will verify logical consistency, clarify core research problems, and develop the communication skills necessary to convey research findings effectively. Regardless of the timing of enrollment or academic year, this course provides opportunities for reporting and discussion tailored to each student's progress. The goal is to acquire the essential communicative competence required of a professional researcher.
Class Schedule	lesson1: Research Ethics and Annual Planning lesson2: Literature Review 1 (In-depth reading and critical examination of major recent papers) lesson3: Literature Review 2 (In-depth reading and critical examination of major recent papers) lesson4: Literature Review 3 (In-depth reading and critical examination of major recent papers) lesson5: Research Design 1 (Research plans for April enrollees / Advanced proposals for October enrollees) lesson6: Research Design 2 (Research plans for April enrollees / Advanced proposals for October enrollees) lesson7: Research Design 3 (Research plans for April enrollees / Advanced proposals for October enrollees) lesson8: Methodology Review 1 (Evaluation of experimental design validity) lesson9: Methodology Review 2 (Evaluation of experimental design validity) lesson10: Methodology Review 3 (Evaluation of experimental design validity) lesson11: Interpreting Results and Defining "Kansei Value" lesson12: Mid-term Presentations 1 (Progress reports and Q&A sessions) lesson13: Mid-term Presentations 2 (Progress reports and Q&A sessions) lesson14: Mid-term Presentations 3 (Progress reports and Q&A sessions) lesson15: First-term Summary (Feedback for the next term)
Text/Reference Books,etc.	The Elements of Statistical Learning
PC or AV used in Class,etc.	Text, Handouts, Audio Materials, Visual Materials
(More Details)
Learning techniques to be incorporated	Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning
Suggestions on Preparation and Review	Functional MRI (fMRI) is an extremely powerful research method that allows for the non-invasive investigation of cognitive functions in living humans. Resting-state fMRI, in particular, is known to be useful for clinical diagnosis and shows great promise for medical applications. Students are expected to learn fMRI data analysis methods, understand the specific characteristics of this data, and become capable of applying appropriate machine learning algorithms.
Requirements
Grading Method	Grading will be based on the student's level of understanding regarding the nature of fMRI data and the characteristics of various machine learning algorithms. Contribution to Class Exercises -Content of the Final Presentation -Contribution to Class Exercises -Content of the Final Presentation
Practical Experience
Summary of Practical Experience and Class Contents based on it
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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