2023 Faculty Courses School of Computing Major courses
Progressive Applied Artificial Intelligence and Data Science A
- Academic unit or major
- Major courses
- Instructor(s)
- Asako Kanezaki / Norio Tomii / Yoshihiro Miyake / Isao Ono / Katsumi Nitta / Takao Kobayashi / Nobuyuki Koyama / Kenji Aoyagi / / Kousaku Igawa / Takayoshi Tsuchimoto / Takashi Okada / Tsukasa Kanai / Yoshitaka Okazaki / Kei Furukawa / Hironori Tanji
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue
- Class
- -
- Course Code
- XCO.T687
- Number of credits
- 100
- Course offered
- 2023
- Offered quarter
- 3Q
- Syllabus updated
- Jul 8, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course is designed for students to understand the outline of WEB media systems focusing on the infrastructure of artificial intelligence and data utilization, information retrieval, and machine learning 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, and also through the opportunity for students to describe their own ideas.
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 lectures are given by scientists or engineers from Daiichi-Snakyo, Toppan, Yahoo, Komatsu,
Shimizu Construction and Sumitomo Mitsui Trust Bank about application of AI and Data Science to the practical systems.
Keywords
Data utilization, big data, machine learning, artificial intelligence, data science
Competencies
- 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 submit a summary report after each class and a total report after the final class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Data science for new drug development in pharmaceutical industry | How data science can contribute to new drug development in pharmaceutical industry will be explained in this lecture. |
| Class 2 | The applicability of AI and data science in life science | This lecture will discuss how AI and machine learning can contribute to the development of life sciences. |
| Class 3 | Utilization of Data Science and AI in Toppan DX | This lecture will focus on the research and development of image/document R&D and manufacturing data analysis/solution development cases in the printing business. We aim to understand data utilization in the manufacturing industry. |
| Class 4 | Data Utilization at Yahoo! JAPAN | Sharing AI/Data Utilization Cases at Yahoo! JAPAN |
| Class 5 | Creating Customer Value with DX, AI, and IoT: Introduction of Construction Tech Examples | Introduce how ICT is used to solve real business problems through construction tech case examples |
| Class 6 | AI and Data Application in the Construction Industry | Learn the importance of digitalization through examples of AI and data utilization initiatives. |
| Class 7 | Impact Orientation and Scientific Thinking in ESG Finance | Learning from the logical perspective of impact (societal outcomes) and the cutting-edge domain of ESG finance where science and finance merge. |
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 required
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and shared in the Zoom lecture
Evaluation methods and criteria
Reports at the end of 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
Prerequisites
Only students of doctor course are acceptable.
Other students are required to register XCO.T483 instead of XCO.T687.
Other
This lecture is offered in cooperation with Daiichi Sankyo, Toppan, Yahoo! JAPAN, LINE, Komatsu, and Shimizu Corporation.