▶Japanese
Post to SNS
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Kanezaki Asako Tomii Norio Ono Isao Miyazaki Kei Okumura Keiji Sakuma Jun Nitta Katsumi Miyake Yoshihiro
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- -
- Group
- -
- Course number
- DSA.P631
- Credits
- 1
- Academic year
- 2024
- Offered quarter
- 3Q
- 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 will be given by lecturers from companies such as Kawasaki Heavy Industries Ltd., TeamLab Inc., DIC Corp.,The Bank of Tokyo-Mitsubishi UFJ ltd., Takenaka Corp., Electronic Power Development Co. Ltd. Please note that the companies giving the lectures are subject to change. |
Keywords
Data utilization, big data, machine learning, artificial intelligence, data science, heavy industries, digital art, materials, finance, construction, electric power
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 | Case Studies of Data Science and AI Implementations in the heavy industries | Understanding Data Science and AI Implementation Case Studies in the heavy industries |
| Class 2 | Data Science and AI Application Cases in Digital Art (1) | Understanding Data Science and AI Applications in Digital Art |
| Class 3 | Data Science and AI Application Cases in Digital Art (2) | Understanding Data Science and AI Applications in Digital Art |
| Class 4 | Application Examples of Data Science and AI in Chemical Company | Understanding Data Science and AI Applications in Chemical Companies |
| Class 5 | Applications of Data Science and AI in Financial Companies | Understanding Data Science and AI Applications in Financial Companies |
| Class 6 | Application of Data Science and AI in a Construction Company | Understanding Data Science and AI Applications in Construction Companies |
| Class 7 | Application of Data Science and AI in Electric Power Companies | Understanding Data Science and AI Applications in Electric Power Companies |
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.
No final exam will be given. The evaluation will be based on the reports of each assignment.
Assessment criteria and methods
Reports at the end of each class and a term-end report will be considered
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.)
Only students of doctor curse are acceptable. Other students must take DSA.P431 " Applied Practical Data Science and AI 3A" 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 syllabus may be revised before the start of the third quarter.
・This course corresponds to Progressive Applied AI and Data Science A (XCO.T687), which was offered until FY2023. Students who took Progressive Applied AI and Data Science A may not register for this course.
