| Academic Year |
2024Year |
School/Graduate School |
Common Graduate Courses (Doctoral Course) |
| Lecture Code |
8E550102 |
Subject Classification |
Common Graduate Courses |
| Subject Name |
データサイエンス |
Subject Name
（Katakana） |
データサイエンス |
Subject Name in
English |
Data Science |
| Instructor |
NAMEKAWA YUSUKE |
Instructor
(Katakana) |
ナメカワ ユウスケ |
| Campus |
Across Campuses (videoconferencing, etc.) |
Semester/Term |
1st-Year, Second Semester, 4Term |
| Days, Periods, and Classrooms |
(4T) Inte：Online |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Video Lecture |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
J
:
Japanese |
| Course Level |
5
:
Graduate Basic
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
machine learning, python |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | This course is one of the elective subjects in the category of "Career Development and Data Literacy Courses" for Common Graduate Courses. This category of courses aims to provide opportunities for students to learn about the development of the current social systems, to gain knowledge needed for the future, to concretely tackle the challenges facing modern society, and to acquire the ability to utilize knowledge and skills. |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
This course offers an opportunity to learn the basics of machine learning by python. |
| Class Schedule |
Introduction
A Python Tutorial
Python Libraries
Numerical Analysis
Machine Learning
Assignments and Quizzes in moodle. |
Text/Reference
Books,etc. |
Not Specified. |
PC or AV used in
Class,etc. |
|
| (More Details) |
Lecture Videos |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Students can view lecture videos any time. |
| Requirements |
|
| Grading Method |
Post-class Reports |
| 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. |