2023 Progressive Applied Artificial Intelligence and Data Science D

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Academic unit or major
School of Computing
Kanezaki Asako  Tomii Norio  Miyazaki Kei  Okumura Keiji  Sakuma Jun  Ono Isao  Miyake Yoshihiro  Nitta Katsumi  Kobayashi Takao  Tamura Tetsuya  Yamada Takeshi  Fujimoto Shotaro  Hayashi Kidai  Yoshimoto Seiya  Nishida Daishiro  Hiyama Takumi  Fujihira Ryo  Suzuki Takashi  Dairiki Ryo  Kitsunezuka Masaki  Yamanaka Masao  Seki Koichi  Mori Yuto 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Day/Period(Room No.)
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

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.

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.

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.


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

Online lecture via Zoom.
It is required to submit a report after each class and a total report after the final 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 AI and Data Science in the Automotive Enterprise (1) Understand the application of AI and data science in a automotive company
Class 4 AI and Data Science in the Automotive Enterprise (2) Understand the application of AI and data science in a automotive company
Class 5 Human Augmentation Human augmentation, which enhances human potential and enables the acquisition of new abilities using IT technologies such as AI and IoT, will be explained with various examples.
Class 6 AI and Data Science in the Pharmaceutical Industry Repport assignment: In the future, how advanced will the use of data science in drug discovery research be, and what kind of relationship will be established between human researchers and research AI?
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.



Reference books, course materials, etc.

Materials will be provided on T2SCHOLA in advance and projected in the class room

Assessment criteria and methods

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 (i.e., required knowledge, skills, courses, etc.)

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.


This lecture is supported by Team-labo Inc, Toyota Inc., Kyocela Inc., Esai Inc., and Tokyo Electron Inc.
Online lecture using Zoom.

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