TB151203 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 TB151203 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, Second Semester, Second Semester

Days, Periods, and Classrooms (2nd) Inte

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

Lecture-based, Exercise-based, Discussion, Student presentations, In-class work, Programming

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 Neuroscience, fMRI, Machine Learning, Sensation, Perception, Emotion, Affect, Deep Learning

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 cultivate the communication skills necessary to disseminate research findings effectively.
Regardless of enrollment timing 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 Setting Milestones for the Second Term
lesson2 Progress Reports 1 (Interpretation and discussion of analysis results)
lesson3 Progress Reports 2 (Interpretation and discussion of analysis results)
lesson4 Progress Reports 3 (Interpretation and discussion of analysis results)
lesson5 Logical Structuring 1 (Evaluation of the manuscript's storyline and validity)
lesson6 Logical Structuring 2 (Evaluation of the manuscript's storyline and validity)
lesson7 Logical Structuring 3 (Evaluation of the manuscript's storyline and validity)
lesson8 Interim Summary (Logical reinforcement of research findings)
lesson9 Dissemination Readiness 1 (Preparation of conference abstracts/proceedings)
lesson10 Dissemination Readiness 2 (Preparation of conference abstracts/proceedings)
lesson11 Dissemination Readiness 3 (Preparation of conference abstracts/proceedings)
lesson12 Manuscript Preparation 1 (Refining the paper based on conference feedback)
lesson13 Manuscript Preparation 2 (Refining the paper based on conference feedback)
lesson14 Manuscript Preparation 3 (Refining the paper based on conference feedback)
lesson15 Outlook for the Next Academic Year and Degree Application

Text/Reference
Books,etc. The Elements of Statistical Learning（Hastie et al. ）
Functional Magnetic Resonance Imaging (Huettel et al.）

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 Students must acquire a solid understanding of the fundamentals of statistics and machine learning. Particular emphasis is placed on gaining the foundational knowledge necessary to avoid common pitfalls in machine learning applications.

It is essential to correctly understand typical issues such as overfitting and data leakage. Through the process of conducting fMRI experiments, students will learn how these issues can compromise research integrity and how to mitigate them.

Requirements

Grading Method Evaluation will be based on the student's research attitude and the depth of their understanding regarding statistics and machine learning.

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	TB151203	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, Second Semester, Second Semester
Days, Periods, and Classrooms	(2nd) Inte
Lesson Style	Seminar	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)
Lecture-based, Exercise-based, Discussion, Student presentations, In-class work, Programming
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	Neuroscience, fMRI, Machine Learning, Sensation, Perception, Emotion, Affect, Deep Learning
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 cultivate the communication skills necessary to disseminate research findings effectively. Regardless of enrollment timing 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 Setting Milestones for the Second Term lesson2 Progress Reports 1 (Interpretation and discussion of analysis results) lesson3 Progress Reports 2 (Interpretation and discussion of analysis results) lesson4 Progress Reports 3 (Interpretation and discussion of analysis results) lesson5 Logical Structuring 1 (Evaluation of the manuscript's storyline and validity) lesson6 Logical Structuring 2 (Evaluation of the manuscript's storyline and validity) lesson7 Logical Structuring 3 (Evaluation of the manuscript's storyline and validity) lesson8 Interim Summary (Logical reinforcement of research findings) lesson9 Dissemination Readiness 1 (Preparation of conference abstracts/proceedings) lesson10 Dissemination Readiness 2 (Preparation of conference abstracts/proceedings) lesson11 Dissemination Readiness 3 (Preparation of conference abstracts/proceedings) lesson12 Manuscript Preparation 1 (Refining the paper based on conference feedback) lesson13 Manuscript Preparation 2 (Refining the paper based on conference feedback) lesson14 Manuscript Preparation 3 (Refining the paper based on conference feedback) lesson15 Outlook for the Next Academic Year and Degree Application
Text/Reference Books,etc.	The Elements of Statistical Learning（Hastie et al. ） Functional Magnetic Resonance Imaging (Huettel et al.）
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	Students must acquire a solid understanding of the fundamentals of statistics and machine learning. Particular emphasis is placed on gaining the foundational knowledge necessary to avoid common pitfalls in machine learning applications. It is essential to correctly understand typical issues such as overfitting and data leakage. Through the process of conducting fMRI experiments, students will learn how these issues can compromise research integrity and how to mitigate them.
Requirements
Grading Method	Evaluation will be based on the student's research attitude and the depth of their understanding regarding statistics and machine learning.
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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