| Academic Year |
2026Year |
School/Graduate School |
Graduate School of Biomedical and Health Sciences (Doctoral Course) |
| Lecture Code |
TB151303 |
Subject Classification |
Specialized Education |
| Subject Name |
感性データサイエンス特別研究 |
Subject Name
（Katakana） |
カンセイデータサイエンストクベツケンキュウ |
Subject Name in
English |
Advanced Research 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 |
Experiment |
Lesson Style
(More Details) |
Face-to-face, Online (simultaneous interactive), Online (on-demand) |
| 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 |
5
:
Graduate Basic
|
| 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 |
Students will conduct neuroscience research using machine learning techniques on human physiological data (such as fMRI data), with the ultimate goal of publishing a research paper. |
| Class Schedule |
lesson1 Individual Consultation (Planning for the autumn term and the end of the academic year)
lesson2 Experimentation & Data Collection 1
lesson3 Experimentation & Data Collection 2
lesson4 Experimentation & Data Collection 3
lesson5 Data Analysis 1
lesson6 Data Analysis 2
lesson7 Data Analysis 3
lesson8 Progress Assessment (Completion of data collection)
lesson9 Manuscript Writing 1 (Drafting the dissertation or research paper)
lesson10 Manuscript Writing 2 (Drafting the dissertation or research paper)
lesson11 Manuscript Writing 3 (Drafting the dissertation or research paper)
lesson12 Revision & Refinement 1 (Individualized editing and feedback from the supervisor)
lesson13 Revision & Refinement 2 (Individualized editing and feedback from the supervisor)
lesson14 Revision & Refinement 3 (Individualized editing and feedback from the supervisor)
lesson15 Final Summary (Review of research outcomes and next steps) |
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, Microsoft Teams, Zoom |
| (More Details) |
|
| Learning techniques to be incorporated |
Discussions, Paired Reading, 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. It is crucial to gain the foundational knowledge necessary to avoid common pitfalls in machine learning applications.
Specifically, students must correctly understand typical issues such as overfitting and data leakage, and learn through the process of fMRI experimentation how these problems can arise and compromise research results. |
| 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. |