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- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Kanezaki Asako Tomii Norio Miyazaki Kei Okumura Keiji Sakuma Jun Ono Isao Miyake Yoshihiro Nitta Katsumi Matsui Ryo Igawa Kousaku Goto Yuichi Kawano Yukihiro Kosaka Junichi Tsunashima Koyori Takahashi Tsubasa
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- Day/Period(Room No.)
- Wed7-8(M-B07(H101), J2-302(J233))
- Group
- -
- Course number
- DSA.P622
- Credits
- 1
- Academic year
- 2024
- Offered quarter
- 2Q
- Syllabus updated
- 2024/3/29
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
The purpose of this class 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, lecturers from companies in various fields such as architecture, IT, finance, and materials will introduce case studies of technology and product development using data science and AI.
The goal is for students to gain a broad perspective of the real world by acquiring knowledge about the application of data science and AI technologies in a wide range of fields, and by explaining their considerations about social applications in their assigned reports.
Student learning outcomes
This course aims to develop ability of each student to be more successful in the real world with the consideration of social implementation of data science and artificial intelligence.
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 Recruit, IHI, Line Yahoo, Sumitomo Coop. Komatsu ltd. about application of AI and Data Science to the practical systems. |
Keywords
artificial intelligence, data science, machine learning, AI business, manufacturing, construction machinery, general trading company, heavy industries
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
This course is classified as a high-flex type, but can only be taken in designated classrooms in Ookayama and Suzukakedai.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Business Applications of Machine Learning and Data Science (1) | The difficulties in implementing data science technologies in society and their solutions will be presented with case studies. |
| Class 2 | Business Applications of Machine Learning and Data Science (2) | The difficulties in implementing data science technologies in society and their solutions will be presented with case studies. |
| Class 3 | 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. |
| Class 4 | Data utilization at Yahoo! JAPAN | Sharing AI/Data Utilization Cases at Yahoo! JAPAN |
| Class 5 | DX Strategies for “Sogo Shosha”, General Trading Company, Learning from the Fields— Practical Examples of Data Analysis and AI Utilization. | Understanding DX strategies and use cases of data science and AI in a general trading company. |
| Class 6 | Creating Customer Value in the Construction Industry through ICT: Introduction of DX, AI, and Modern Software Development | Learn how knowledge of software development and AI, acquired in university, is utilized in the development of actual commercial products. |
| Class 7 | R&D on Trustworthy AI at LY Corp | This lecture introduces R&D activities towards actualizing trustworthy AI. |
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.
Textbook(s)
None required.
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance.
Assessment criteria and methods
No final exam will be given. The evaluation will be based on the reports of each assignment.
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 must take DSA.P422 " Applied Practical Data Science and AI 2B" instead of this course.
Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).
Asako Kanezaki, Katsumi Nitta, Norio Tomii
lecture_ap[at]dsai.titech.ac.jp
Office hours
Contact by e-mail in advance to schedule an appointment.
Other
・This class is a technical course that can be considered an entrepreneurship course. The GAs that this subject corresponds to are GA0M and GA1M (added March 29, 2024).
・This course corresponds to Progressive Applied AI and Data Science D (XCO.T690), which was offered until FY2023. Students who took Progressive Applied AI and Data Science D may not register for this course.
