▶Japanese
Post to SNS
- 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 Miura Masahiro Mao Zaixing Akiba Masahiro Yagura Kazuo Nakamichi Keisuke Okada Takashi Tsuchimoto Takayoshi Nishigata Yusuke Tashiro Yusuke Ito Takayuki
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8(M-124, J2-302(J233))
- Group
- -
- Course number
- DSA.P421
- 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 |
|---|---|
| In this course, lecturers from Topcon, Mitsui Fudosan, Fanuc, All Nippon Airways, Toppan Printing, and Mitsubishi UFJ Trust and Banking will lecture on problem-solving techniques based on their practical experience. |
Keywords
Data Science, Artificial Intelligence, Machine Learning, Finance, Robot, transportation, printing, medical equipment
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 | Sensing technology based on optics, and data and processing methods to address social issues in the areas of medicine, food, and housing. | One third of the time will be allocated to introducing the head office, which has developed and manufactured optical sensors as IoT sensors in the field of medicine, food, and housing, and has developed a business adapted to the needs of various industries, and to the contents related to IoT (sensing), while the remaining 2/3 of the time will be devoted to lectures on data science and AI. |
| Class 2 | Data Application in the Mitsui Fudosan Group | Sharing examples of data-enabled marketing at Mitsui Fudosan Tokyo Dome (subsidiaries) |
| Class 3 | Manufacturing and AI - Machine Tools and AI Application | Manufacturing and AI - Machine Tools and AI Application |
| Class 4 | Manufacturing and AI - Industrial Robots and AI Application | To understand the challenges facing manufacturing and the necessity of AI, as well as the real issues and solutions for AI application in the manufacturing field, through specific examples of applications. The presentation will focus on industrial robots. |
| Class 5 | Challenges and future prospects at ANA such as AI development using in-house data | ・Behind-the-scenes of in-flight meal service for premium class passengers on domestic flights ・Development of an AI prediction system for the number of in-flight meals to be loaded ・Shift creation for airport attendants is actually a difficult task. ・POC for shift creation using mathematical optimization technology |
| Class 6 | Application of Data Science and AI in TOPPAN's DX | This lecture aims to understand and master the use of data in the manufacturing industry in addition to how research in academia is introduced into industry, based on examples of image/document system R&D and manufacturing data analysis/solution development in the printing business |
| Class 7 | Utilization of data and AI in trust banks | In this lecture, we will learn about the connection between financial engineering and real-world business through examples of the use of data and AI at "Mitsubishi UFJ Trust and Banking" and "Mitsubishi UFJ Trust Investment Technology Institute" |
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
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Doctoral students must take DSA.P621 "Progressive Applied Practical Data Science and AI 2A".
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 Applied AI and Data Science B (XCO.T484), which was offered until FY2023. Students who took Applied AI and Data Science B as undergraduates should register for this course. Students who took Applied AI and Data Science B in graduate school may not register for this course.
