Course title
310053002
Basics of Artificial Intelligence

 ICHIKAWA Manabu
Middle-level Diploma Policy (mDP)
Program / Major mDP Goals
IoT Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Software Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Media Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Data Science Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Mechatronics Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Architecture and Architectural Engineering Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Environmental Systems and Urban Planning Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Bioscience Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Biomedical Engineering Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Sports Engineering Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Mathematical Sciences Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Purpose of class
Artificial intelligence has become an increasingly important presence in contemporary society, and the range of fields in which it is used is expected to continue expanding. This course aims to broaden students’ understanding of the future use of artificial intelligence by examining how AI is currently applied in society and how it has contributed to solving social issues.
Course description
In this course, students will learn about the history and current state of artificial intelligence, as well as the fields of society and research in which AI is being used. Students will also study the ethical and legal issues surrounding artificial intelligence.
Goals and objectives
  1. Understand examples of how artificial intelligence is used in society.
  2. Understand social issues that have been addressed through artificial intelligence.
  3. Understand methods for using and applying artificial intelligence.
  4. Understand the latest trends in artificial intelligence.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 レポートまたは小課題 Total.
1. 25% 25%
2. 25% 25%
3. 25% 25%
4. 25% 25%
Total. 100% -
Evaluation method and criteria
Students will be evaluated based on the reports submitted for each class. A total score of 60 or higher out of 100 is required to pass the course.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. History of Artificial Intelligence Review the distributed course materials 100minutes
Complete the assignment 100minutes
2. Fields of Application of Artificial Intelligence Review the distributed course materials 100minutes
Complete the assignment 100minutes
3. Artificial Intelligence and Society
Ethics, Laws, and Regulations Related to the Use of Artificial Intelligence 		Review the distributed course materials 100minutes
Complete the assignment 100minutes
4. Fundamentals and Future Prospects of Machine Learning Review the distributed course materials 100minutes
Complete the assignment 101minutes
5. Fundamentals and Future Prospects of Deep Learning Review the distributed course materials 100minutes
Complete the assignment 100minutes
6. Fundamentals and Future Prospects of Generative AI Review the distributed course materials 100minutes
Complete the assignment 100minutes
7. Development and Operation of AI Systems Review the distributed course materials 100minutes
Complete the assignment 100minutes
8. 0minutes
9. 0minutes
10. 0minutes
11. 0minutes
12. 0minutes
13. 0minutes
14. 0minutes
Total. - - 1401minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback outside of the class (ScombZ, mail, etc.)
Textbooks and reference materials
資料：コンソーシアム教材資料
Prerequisites
None
Office hours and How to contact professors for questions
  • Ask your classroom teacher
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
About half of the classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 1.NO POVERTY
  • 2.ZERO HUNGER
  • 3.GOOD HEALTH AND WELL-BEING
  • 4.QUALITY EDUCATION
  • 5.GENDER EQUALITY
  • 6.CLEAN WATER AND SANITATION
  • 7.AFFORDABLE AND CLEAN ENERGY
  • 8.DECENT WORK AND ECONOMIC GROWTH
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
  • 10.REDUCED INEQUALITIES
  • 11.SUSTAINABLE CITIES AND COMMUNITIES
  • 12.RESPONSIBLE CONSUMPTION & PRODUCTION
  • 13.CLIMATE ACTION
  • 14.LIFE BELOW WATER
  • 15.LIFE ON LAND
  • 16.PEACE, JUSTICE AND STRONG INSTITUTIONS
  • 17.PARTNERSHIPS FOR THE GOALS
Last modified : Sat Aug 29 04:02:27 JST 2026