| Academic Year |
2024Year |
School/Graduate School |
Education and Research Center for Artificial Intelligence and Data Innovation |
| Lecture Code |
8J030001 |
Subject Classification |
Specialized Education |
| Subject Name |
AI基礎 |
Subject Name
（Katakana） |
エーアイキソ |
Subject Name in
English |
Basics of AI |
| Instructor |
EGUCHI KOJI,FURUI AKIRA,YU YI |
Instructor
(Katakana) |
エグチ コウジ,フルイ アキラ,ユ イ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
2nd-Year, Second Semester, Intensive |
| Days, Periods, and Classrooms |
(Int) Inte：Online |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Lectures |
| Credits |
1.0 |
Class Hours/Week |
|
Language of Instruction |
J
:
Japanese |
| Course Level |
2
:
Undergraduate Low-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
2nd Year and above |
| Keywords |
Machine learning, deep learning, natural language processing, robot control, pattern recognition |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | This lecture course is compulsory within the Specific Program of Basics and Applications of AI and Data Science. This lecture course aims to develop the students' basic knowledge and skills of AI. |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
The objectives of this lecture course are for students to understand the basic concepts of machine learning (including supervised and unsupervised learning), deep learning, and reinforcement learning, and to learn their applications to natural language processing, pattern recognition, and robot control. |
| Class Schedule |
Lesson 1. Introduction: History and applications of AI, and its relationships with society
Lesson 2. Basics of machine learning
Lesson 3. Applications of machine learning
Lesson 4. Natural language processing
Lesson 5. Pattern recognition
Lesson 6. Neural networks
Lesson 7. Deep learning and AI systems
Lesson 8. AI and robots
Short quizzes will be conducted in each lecture. |
Text/Reference
Books,etc. |
Handouts will be provided by the lecturers. |
PC or AV used in
Class,etc. |
|
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Each lecture will be posted as on-demand videos. Handouts will also be provided along with each lecture to encourage you to review them before and after the lecture. |
| Requirements |
This lecture course is compulsory within the Specific Program of Basics and Applications of AI and Data Science. This lecture course aims to develop the students' basic knowledge and skills of AI. |
| Grading Method |
Your evaluation will be based on your performance of short quizzes conducted in each lecture. |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
This lecture course will be offered in the 4th term. Basically, lecture materials (including handouts, lecture videos, and short quizzes) will be uploaded on Moodle for each lecture every week. Online meetings will be available to answer questions using Teams. Detailed schedule for the delivery of lecture materials will be announced on the message board in My-Momiji. |
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. |