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- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Kanezaki Asako Murata Tsuyoshi Tomii Norio Miyazaki Kei Okumura Keiji Sakuma Jun Nitta Katsumi Ono Isao Miyake Yoshihiro Kiyota Yoshihisa Fukui Motofumi Kaji Yusuke
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- Day/Period(Room No.)
- Fri7-8(M-B07（H101）, J2-302(J233))
- Group
- -
- Course number
- DSA.P413
- Credits
- 1
- Academic year
- 2024
- Offered quarter
- 1Q
- 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.
Therefore, in addition to the seven class sessions, this course emphasizes dialogue with company lecturers, and in principle, students shall participate in the DS&AI Forum to be held face-to-face on the Ookayama campus in the afternoon of June 3, 2024. (Added on March 29, 2024)
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 |
|---|---|
| In this course, lecturers from Furukawa Electric, Sumitomo Heavy Industries, Mitsubishi Electric, Rakuten Group will lecture on problem-solving techniques based on their practical experience. |
Keywords
Data Science, Artificial Intelligence, Machine Learning, materials, heavy equipment, electric machinery, IT
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 | Data Utilization in Manufacturing (Lecture in English) | Understanding DX through examples of data utilization and digitalization in manufacturing |
| Class 2 | Information technology for heavy machinery. | Relationships and issues between heavy machinery, people, and information technology. |
| Class 3 | Industrial application of artificial intelligence technology | In this course, practical application examples of artificial intelligence technology will be introduced. Through the understanding of practical examples, students will acquire appropriate selection skills for algorithms according to the task. |
| Class 4 | 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 5 | Large Language Models and Cognitive Architecture | How to build robust cognitive architecture for LLM applications |
| Class 6 | R&D projects in Rakuten Group | In this lecture, we’d like to introduce the application of research outcomes in actual services at Rakuten. |
| Class 7 | Actual Planning and Promotion of DX in Manufacturing Companies | Planning and promotion of digital technology introduction will be explained based on actual cases. |
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.
The evaluation will also include the results of participation in the DSAI Forum to be held on June 3, 2024. (Added on March 29, 2024)
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 (i.e., required knowledge, skills, courses, etc.)
Doctoral students must take DSA.P613 "Progressive Applied Practical Data Science and AI 1C".
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 Practical AI and Data Science A (XCO.T493), which was offered until FY2023. Students who took Practical AI and Data Science A as undergraduates should register for this course. Students who took Practical AI and Data Science A in graduate school may not register for this course.
