| Academic Year |
2025Year |
School/Graduate School |
Common Graduate Courses (Doctoral Course) |
| Lecture Code |
8E550101 |
Subject Classification |
Common Graduate Courses |
| Subject Name |
Data Science |
Subject Name
（Katakana） |
データサイエンス |
Subject Name in
English |
Data Science |
| Instructor |
|
Instructor
(Katakana) |
|
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, Second Semester, 3Term |
| Days, Periods, and Classrooms |
(3T) Tues9-10,Weds7-8：IMC-Main 2F PC Rm |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Online (simultaneous interactive), Online (on-demand) |
| |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
J
:
Japanese |
| Course Level |
5
:
Graduate Basic
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
01
:
Mathematics/Statistics |
| Eligible Students |
|
| Keywords |
Python, numerical computation, optimization methods, machine learning, model evaluation |
| 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 aims to provide a fundamental understanding of machine learning concepts. Through exercises using the Python programming language, students will deepen their comprehension of key techniques and their practical applications. |
| Class Schedule |
Introduction & Python Basics
1.Introduction: Overview of Data Science
2.Python Fundamentals 1: Arithmetic Operations, Data Types, Operators
3.Python Fundamentals 2: Functions, Loops, Randomization
4.Python Libraries 1: Introduction to NumPy for Numerical Computation
5.Python Libraries 2: Data Processing with Pandas
6.Python Libraries 3: Data Visualization with Matplotlib
7.Python Libraries 4: Basics of scikit-learn and CVXOPT
Numerical Analysis & Optimization
8.Numerical Analysis 1: Gradient Descent, Newton’s Method
9.Numerical Analysis 2: Linear Programming, Quadratic Programming
Machine Learning & Model Evaluation
10.Machine Learning 1: Linear Regression, Ridge Regression, Lasso Regression, Polynomial Regression
11.Machine Learning 2: Decision Trees, Support Vector Machines (SVM)
12.Machine Learning 3: Generalization Performance, Bias-Variance Tradeoff
13.Machine Learning 4: Learning Curves, Cross-Validation
14.Machine Learning 5: Confusion Matrices and Model Evaluation
Final Review & Advanced Topics
15.Final Review: Recap and Exploration of Advanced Topics
|
Text/Reference
Books,etc. |
No specific textbook is required. |
PC or AV used in
Class,etc. |
Handouts, Audio Materials, Visual Materials |
| (More Details) |
|
| Learning techniques to be incorporated |
Post-class Report |
Suggestions on
Preparation and
Review |
Students should utilize the lecture videos for reviewing concepts and reinforcing understanding. |
| Requirements |
|
| Grading Method |
Viewing the lecture videos is a requirement for course credit. Final grades will be determined based on assignments and 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. |