2023 Faculty Courses School of Computing Major courses
Progressive Applied Artificial Intelligence and Data Science B
- Academic unit or major
- Major courses
- Instructor(s)
- Asako Kanezaki / Norio Tomii / Kei Miyazaki / Keiji Okumura / Jun Sakuma / Isao Ono / Yoshihiro Miyake / Katsumi Nitta / Takao Kobayashi / Hideki Akashika / Yusuke Kaji / Yu Hirate / Yusuke Nishizawa / Koichi Iwakabe / Koichi Okada / Yukihiro Kawano
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue
- Class
- -
- Course Code
- XCO.T688
- Number of credits
- 100
- Course offered
- 2023
- Offered quarter
- 2Q
- Syllabus updated
- Jul 8, 2025
- Language
- Japanese
Syllabus
Course overview and goals
The purpose of this course is to understand the current status and state-of-the-art of social implementation of AI and data science technologies, and to examine the applicability and challenges of these technologies. In each class session as indicated in the lesson plan, trends and issues in technology and product development in the fields of IT, telecommunications, architecture, chemical industry, heavy industry, etc. will be explained.
Course description and aims
この授業科目は、様々な分野のAIやデータサイエンス技術に関する知識を獲得し、課題レポートによって社会応用に関する考察や新たな着想を説明することによって、受講生が実社会において活躍する広い視野を得ることを目標にしている。
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 the practical experience of engineers from companies (Rakuten Group, Inc., Kajima Corporation, Mitsui Chemicals, Inc., Nippon Telegraph and Telephone East Corporation, and IHI Corporation) who are implementing data science and artificial intelligence technologies in society.
Keywords
Data science, artificial intelligence, deep learning, machine learning, IT companies, telecommunications, construction, chemical industry, heavy industry
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Class1－Class7：Lecture
This course requires students to take an active role in their own learning. It is required to submit a summary report after each class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Notes and development examples for building large-scale web services | The lecture will introduce the contents to be considered and matters to be noted when building large-scale Web services, based on case studies, as well as examples of development in payments. |
| Class 2 | Career development in the data science and ML field | Lecture on how project experience in a company can help you to become a data scientist or ML engineer. |
| Class 3 | Examples of AI-related R&D at Rakuten Group | Using examples of AI-related R&D at Rakuten Group, the presentation will show how corporate R&D organizations contribute to business. |
| Class 4 | Data Science and AI Applications in Architectural Firms | Presenting a case study of the use of data science and AI in an architectural firm |
| Class 5 | Using Data Science in R&D for an Integrated Chemical Company | With the prosperity of data science, companies are also making efforts to utilize data science in their business. In this lecture, we will introduce the approaches and applications of "digital science," including computational chemistry and CAE, in the research and development of materials. A report summarizing students' impressions of the lecture content will be assigned as an assignment for this lecture. |
| Class 6 | Applying a challenge of data utilization hackathon to real business | Hackathons are not just competitions to realize ideas through coding, but good opportunities to improve skills and chances to create your work. And we can apply them to real business. The lecturer of this course, a successive three-year first-prize winner of an international hackathon "Asia Opendata Challenge," introduces a case of how he applied his work for the business of NTT East, his workplace. And you can learn actual cases of business analytics in this lecture. |
| Class 7 | 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. |
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 Zoom lecture
Evaluation methods and criteria
Attendance, assignment reports, and Report at the end of the course will be considered
Related courses
- XCO.T687 ： Progressive Applied Artificial Intelligence and Data Science A
- XCO.T689 ： Progressive Applied Artificial Intelligence and Data Science C
- XCO.T690 ： Progressive Applied Artificial Intelligence and Data Science D
Prerequisites
This course is for doctoral course students. Other students are required to register for Advanced Artificial Intelligence and Data Science B (XCO.T484).