| Academic Year |
2026Year |
School/Graduate School |
Graduate School of Biomedical and Health Sciences (Master’s Course) |
| Lecture Code |
TB000243 |
Subject Classification |
Specialized Education |
| Subject Name |
特別研究 |
Subject Name
（Katakana） |
トクベツケンキュウ |
Subject Name in
English |
Research |
| 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） |
26
:
Biological and Life Sciences |
| Course Area（Discipline） |
04
:
Life 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 physiological data, such as human fMRI data, with the goal of publishing a research paper. |
| Class Schedule |
lesson1 Individual Progress Review and Target Setting for the Second Term
lesson2 Advanced Refinement of Analysis Pipelines 1
lesson3 Advanced Refinement of Analysis Pipelines 2
lesson4 Collection and Organization of Supplementary Data
lesson5 Analysis of Research Findings 1
lesson6 Analysis of Research Findings 2
lesson7 Evaluation and Discussion of Research Results 1
lesson8 Evaluation and Discussion of Research Results 2
lesson9 Preparation of Conference Presentation Materials 1
lesson10 Preparation of Conference Presentation Materials 2
lesson11 Academic Writing Guidance 1 (Manuscript preparation)
lesson12 Academic Writing Guidance 2 (Manuscript preparation)
lesson13 Academic Writing Guidance 3 (Manuscript preparation)
lesson14 Final Adjustments for the Research Reporting Session
lesson15 Summary and Identification of Tasks for the Next Academic Year |
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 |
To understand the fundamentals of statistics and machine learning.
To acquire the foundational knowledge necessary to avoid common pitfalls, such as the misuse of machine learning techniques.
To correctly understand typical problems such as overfitting and data leakage.
To learn how these issues can cause problems in the context of conducting fMRI experiments. |
| Requirements |
|
| Grading Method |
Evaluation will be based on the research attitude and the depth of understanding in 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. |