| Academic Year |
2025Year |
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 |
Instructor
(Katakana) |
チカゾエ ジュンイチ |
| 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 |
The objective of this course is to apply machine learning to human fMRI data to investigate neuroscience-related questions and to publish the results as a research paper. |
| Class Schedule |
lesson1 Introduction to Machine Learning (1)
lesson2 Introduction to fMRI (1)
lesson3 Introduction to Machine Learning (2)
lesson4 Introduction to fMRI (2)
lesson5 Introduction to Machine Learning (3)
lesson6 Introduction to fMRI (3)
lesson7 Research Planning and Execution (1)
lesson8 Research Planning and Execution (2)
lesson9 Research Planning and Execution (3)
lesson10 Research Planning and Execution (4)
lesson11 Research Planning and Execution (5)
lesson12 Analysis and Presentation of Research Results (1)
lesson13 Analysis and Presentation of Research Results (2)
lesson14 Analysis and Presentation of Research Results (3)
lesson15 Analysis and Presentation of Research Results (4) |
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. |