2022 Faculty Courses School of Computing Major courses
Progressive Applied Artificial Intelligence and Data Science D
- Academic unit or major
- Major courses
- Instructor(s)
- Asako Kanezaki / Yoshihiro Miyake / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Tetsuya Tamura / Takeshi Yamada / Shotaro Fujimoto / Kidai Hayashi / Seiya Yoshimoto / Daishiro Nishida / Yuya Nakano / Hayato Tomita / Takumi Hiyama / Masao Yamanaka / Shintaro Fukushima / Shunji Miyahara / Tsunehiko Kamachi / Ryo Dairiki / Tsuyoshi Moriya
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 9-10 Tue
- Class
- -
- Course Code
- XCO.T690
- Number of credits
- 100
- Course offered
- 2022
- Offered quarter
- 2Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course is designed for doctoral course 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.
Course description and aims
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.
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)
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
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Online lecture via Zoom.
It is required to submit a report after each class and a total report after the final class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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. |
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 projected in the class room
Evaluation methods and criteria
Mainly short report required in each class and a term-end report 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 Advanced Artificial Intelligence and Data Science A
Prerequisites
Only students of doctor curse are acceptable.
Other students are required to register XCOT.T486 "Advanced Artificial Intelligence and Data Science A" instead of this course.
Other
This lecture is supported by Team-labo Inc, Toyota Inc., Kyocela Inc., Esai Inc., and Tokyo Electron Inc.
Online lecture using Zoom.