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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 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
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- 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 Tomii Norio Miyazaki Kei Okumura Keiji Sakuma Jun Ono Isao Miyake Yoshihiro Nitta Katsumi Kobayashi Takao Akashika Hideki Kaji Yusuke Hirate Yu Nishizawa Yusuke Iwakabe Koichi Okada Koichi Kawano Yukihiro
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8()
- Group
- -
- Course number
- XCO.T688
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 2Q
- Syllabus updated
- 2023/3/20
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
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.
Student learning outcomes
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。
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| 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 that will be developed
| ✔ 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/Required learning
| Course schedule | Required learning | |
|---|---|---|
| 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. |
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
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and projected in the Zoom lecture
Assessment criteria and methods
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 (i.e., required knowledge, skills, courses, etc.)
This course is for doctoral course students. Other students are required to register for Advanced Artificial Intelligence and Data Science B (XCO.T484).
