2022 Faculty Courses School of Computing Major courses
Practical Artificial Intelligence and Data Science A
- Academic unit or major
- Major courses
- Instructor(s)
- Tsuyoshi Murata / Katsumi Nitta / Takao Kobayashi / Hiroshi Nagahashi / Yoshihiro Miyake / Yoshiyuki Kobayashi / Takayuki Nakata / Hirohisa Tasaki / Daisuke Kitayama / Yukihiro Kawano / Takao Saito
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Fri
- Class
- -
- Course Code
- XCO.T493
- Number of credits
- 100
- Course offered
- 2022
- Offered quarter
- 1Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
The purpose of this course is to understand the current status of social implementation of AI and data science technologies and cutting-edge technologies, and to examine the applicability and challenges of these technologies. Trends and issues in technology and product development in the fields of IT, materials, manufacturing, heavy industry, etc. will be explained in each class as shown in the course schedule.
Course description and aims
The goal of this course is for students to acquire knowledge of AI and data science technologies in various fields, and to gain a broader perspective that will enable them to play an active role in the real world by discussing social applications and explaining new ideas in assignment reports.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
The purpose of this lecture is to introduce practical experience to engineers of companies (Sony Inc., NGC Insulators Inc., Mitsubishi Electric Inc., AGC Inc., IHI Inc. and Asahi Kasei Inc.) engaged in social implementation of data science and artificial intelligence technology.
Keywords
Data Science, Artificial Intelligence, Deep Learning, Machine Learning, Material, Manufacturing Industry, Heavy Industry
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Class1－Class7： Lectures
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Promotion and Application of Deep Learning at Sony (1) | Learn about the current state of AI from a corporate perspective and examples of companies' initiatives for the AI era |
| Class 2 | Promotion and Application of Deep Learning at Sony (2) | Learn about the current state of AI from a corporate perspective and examples of companies' initiatives for the AI era |
| Class 3 | Challenge to data-driven manufacturing | Introduce and understand the efforts and activity examples of data scientists to promote the utilization of data in manufacturing sites |
| Class 4 | Industrial Applications of Artificial Intelligence Technology | Introduce strategies and examples of the utilization of artificial intelligence in the field of electronics, which Japan has strengths in, and acquire the knowledge necessary for engaging in the practical application of artificial intelligence technology. |
| Class 5 | Compatibility between AI and data science and the manufacturing of materials | Learn about examples of AI and data science technologies in the material manufacturing industry and consider the differences from other industries as application destinations. |
| Class 6 | Application of AI/Data Analysis Technology in Heavy Industries | Understand how ai/data analysis is used in the manufacturing industry and examples. As main contents, abnormality diagnosis technology, text analysis technology, and deterioration diagnosis technology are taken up. |
| Class 7 | AI and Data Science in Material Development and Manufacturing | Give an overview of Materials Informatics and some examples of applications, and discuss the current challenges. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
none
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and shared in the Zoom lecture
Evaluation methods and criteria
Mainly short report required in each class will be considered
Related courses
- XCO.T487 ： Fundamentals of data science
- XCO.T488 ： Exercises in fundamentals of data science
- XCO.T489 ： Fundamentals of artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
- XCO.T483 ： Advanced Artificial Intelligence and Data Science A
- XCO.T484 ： Advanced Artificial Intelligence and Data Science B
- XCO.T485 ： Advanced Artificial Intelligence and Data Science C
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
Prerequisites
Priority may be given to students enrolled in the Progressive Graduate Minor in Data Science and Artificial Intelligence.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Please see https://sites.google.com/view/tokyotechdsai/jissen .
Other
This course is supported by Sony Inc., NGC Insulators Inc., Mitsubishi Electric Inc., AGC Inc., IHI Inc. and Asahi Kasei Inc.
Slide distribution and report acceptance will be done by T2SCHOLA. For more information, please refer to the following site.
https://sites.google.com/view/tokyotechdsai/jissen