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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
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- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Kanezaki Asako Miyake Yoshihiro Nitta Katsumi Kobayashi Takao Nagahashi Hiroshi Tamura Tetsuya Yamada Takeshi Fujimoto Shotaro Hayashi Kidai Yoshimoto Seiya Nishida Daishiro Nakano Yuya Tomita Hayato Hiyama Takumi Yamanaka Masao Fukushima Shintaro Miyahara Shunji Kamachi Tsunehiko Dairiki Ryo Moriya Tsuyoshi
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue9-10()
- Group
- -
- Course number
- XCO.T486
- Credits
- 1
- Academic year
- 2022
- Offered quarter
- 2Q
- Syllabus updated
- 2022/5/9
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course is designed for students to understand the outline of artificial intelligence and data science in the digital art and manufacturing industry to consider the possibility to utilize artificial intelligence and data science in the field.
The lecturers will explain broad pictures and recent trends of the topic in each class, as shown below.
Student learning outcomes
This course aims to develop ability of each student to be more successful in the real world with the consideration of artificial intelligence and data science, and also through the opportunity for students to describe their own ideas.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| This lecture is given by cooperate scientists or engineers of Team-Labo Inc., Toyota Inc., Kyocela Inc., Eisai Inc. and Tokyo Electron Inc., about application of AI and Data Science to the practical systems. |
Keywords
artificial intelligence, data science, AI business, digital art, manufacturing
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
This course requires students to take an active role in their own learning. It is required to attend each class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | AI application in digital art (1) | Understand the overview of AI-based artwork and how it works |
| Class 2 | AI application in digital art (2) | Understand the overview of AI-based artwork and how it works |
| Class 3 | Data utilization in mobility (1) | Understand the concept of mobility and related technologies of mobility |
| Class 4 | Data utilization in mobility (2) | Understand the concept of mobility and related technologies of mobility |
| Class 5 | Human Augmentation | Human augmentation, in which technology supplements or improves human abilities or enables the acquisition of new abilities, will be explained with examples of the subject of augmentation and how it can be achieved. |
| Class 6 | AI and Data Science in the Pharmaceutical Industry | Examples of recent applications of AI and data science in the pharmaceutical industry will be explained with some specific case studies. |
| Class 7 | AI and Data Science Create the Future of Semiconductor Manufacturing Equipment | Artificial intelligence, such as machine learning and deep learning, is increasingly being used in semiconductor manufacturing processes. This lecture will introduce state-of-the-art semiconductor manufacturing processes, explain the high technological barriers that stand in the way, and explain how artificial intelligence can be used to overcome these barriers. |
Out-of-Class Study Time (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 required
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and shared in the Zoom lecture
Assessment criteria and methods
Based on reports evaluating students' understanding at the end of each class.
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.T485 ： Advanced Artificial Intelligence and Data Science C
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Students of the doctor course are required to register XCO.T690 "Progressive Applied Artificial Intelligence and Data Science D" instead of XCO.T486.
Other
This lecture is supported by Team-labo Inc, Toyota Inc., Kyocela Inc., Esai Inc., and Tokyo Electron Inc.
Online lecture using Zoom.
