2024 Special graduate degree programs Specially Offered Degree Programs for Graduate Students Center of Data Science and Artificial Intelligence
Applied Practical Data Science and Artificial Intelligence 3A
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Asako Kanezaki / Norio Tomii / Yoshihiro Miyake / Isao Ono / Katsumi Nitta / Kei Miyazaki / Keiji Okumura / Jun Sakuma / Shinichi Nakano / Ryo Fujihira / Munetaka Minoguchi / Kazushige Okayasu / / Takeshi Yamada / Shotaro Fujimoto / Kidai Hayashi / Daishiro Nishida / Kazuki Obi / Masanori Nakagawa / Yusuke Ota / Ryohei Matsuda / Katsuro Tanaka / Tomonori Takahashi
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue
- Class
- -
- Course Code
- DSA.P431
- Number of credits
- 100
- Course offered
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- Mar 14, 2025
- Language
- Japanese
Syllabus
Course overview and goals
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.
This course emphasizes dialogue with corporate lecturers. In addition to the seven class sessions, students shall, in principle, attend the DS&AI Forum to be held on the afternoon of December 2, 2024 at the Oookayama Campus. (Added on September 10, 2024)
Course description and aims
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.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
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.
The following lecture schedule has been revised to be more specific (September 10,2024).
Keywords
Data utilization, big data, machine learning, artificial intelligence, data science, heavy industries, digital art, materials, finance, construction, electric power
Competencies
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Activities to apply AI technology in heavy industries | Explain tips for the success of AI implementation process in the business world |
| Class 2 | AI Application in Digital Art (1) | To understand the outline and mechanism of AI-based art works. |
| Class 3 | AI Application in Digital Art (2) | To understand the outline and mechanism of AI-based art works. |
| Class 4 | How to use data science in chemical materials development | Understand the use of data science in manufacturers and real-world examples |
| Class 5 | Application of Data Science in Financial Market | This session provides overview of applications of data science in foreign exchange market, especially from commercial bank perspective. |
| Class 6 | AI Development for Promoting Digital Transformation (DX) in Construction Companies | This lecture introduces the process and key points as well as the content of AI development for applying AI in business. |
| Class 7 | Utilization of DSAI Knowledge in Electric Power Companies | This lecture will introduce autonomous flying drone development and electricity price forecasting as examples of DSAI applications at J-Power. |
Study advice (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.
Evaluation methods and criteria
No final exam will be given. The evaluation will be based on the reports of each assignment and the participation report of the DS&AI Forum scheduled for December 2. Please note that it is not possible to submit assignment reports for missed lectures. Even if a student submits an assignment report for a lecture he/she has missed, it will not be graded. (Added on September 10, 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
Doctoral students must take DSA.P631 "Progressive Applied Practical Data Science and AI 3A".
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 A (XCO.T483), which was offered until FY2023. Students who took Applied AI and Data Science A as undergraduates should register for this course. Students who took Applied AI and Data Science A in graduate school may not register for this course.