| Academic Year |
2025Year |
School/Graduate School |
Graduate School of Biomedical and Health Sciences (Master’s Course) |
| Lecture Code |
TB000143 |
Subject Classification |
Specialized Education |
| Subject Name |
特別演習 |
Subject Name
（Katakana） |
トクベツエンシュウ |
Subject Name in
English |
Seminar |
| 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 |
Seminar |
Lesson Style
(More Details) |
Face-to-face, Online (simultaneous interactive), Online (on-demand) |
| Hands-on, Discussion-based, Student Presentations, Practical Assignments, 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 |
|
| 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 primary goal is to learn a wide range of machine learning algorithms, including deep learning, in order to select the most appropriate one according to the data's characteristics. This involves analyzing data by applying everything from basic machine learning techniques to advanced deep learning models, such as Large Language Models (LLMs), and being able to correctly interpret the results of these analyses. |
| Class Schedule |
lesson1 Machine Learning Algorithms
lesson2 Neuroscience and Machine Learning (1)
lesson3 Neuroscience and Machine Learning (2)
lesson4 Neuroscience and Machine Learning (3)
lesson5 Neuroscience and Machine Learning (4)
lesson6 Neuroscience and Machine Learning (5)
lesson7 Introduction to Deep Learning
lesson8 fMRI Data Analysis Practice Session (1)
lesson9 fMRI Data Analysis Practice Session (2)
lesson10 fMRI Data Analysis Practice Session (3)
lesson11 Deep Learning Practice Session using LLMs (1)
lesson12 Deep Learning Practice Session using LLMs (2)
lesson13 Deep Learning Practice Session using LLMs (3)
lesson14 Deep Learning Practice Session using LLMs (4)
lesson15 Deep Learning Practice Session using LLMs (5) |
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, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning |
Suggestions on
Preparation and
Review |
fMRI is an extremely useful research method that allows us to non-invasively investigate the cognitive functions of living humans. In particular, resting-state fMRI (rs-fMRI) is known to be useful for diagnosing diseases and is considered promising for clinical applications. It is essential not only to learn fMRI data analysis methods but also to understand the characteristics of this data in order to apply appropriate machine learning algorithms. |
| Requirements |
|
| Grading Method |
Evaluation will be based on the depth of understanding of the properties of fMRI data and the characteristics of machine learning algorithms. |
| 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. |