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- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Kanezaki Asako Nitta Katsumi Tomii Norio Miyazaki Kei Okumura Keiji Sakuma Jun Miyake Yoshihiro Ono Isao Okamoto Masayuki Bunazawa Hideaki Hayashi Takeharu Noguchi Chiriro Horikawa Hiroyuki Nishimura Tomoaki Fukai Hajime Moriya Tsuyoshi Dhaheri Chaima Ono Yuri
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8(M-B07（H101）, J2-302(J233))
- Group
- -
- Course number
- DSA.P611
- 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 Toyota Motor Corporation, JFE Engineering, Tokyo Electron, JERA, and Mitsubishi Research Institute will lecture on problem-solving techniques based on their practical experience. |
Keywords
Data Science, Artificial Intelligence, Machine Learning, Auto mobile, Plant, Semiconductor, AI & Law, electric power development
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 | Technology Development and Prospects related to Artificial Intelligence / Big Data required for the mobility(1) | Understanding the technology and future prospects of mobility by introducing the cases of TOYOTA |
| Class 2 | Technology Development and Prospects related to Artificial Intelligence / Big Data required for the mobility(2) | Understanding the technology and future prospects of mobility by introducing the cases of TOYOTA |
| Class 3 | Utilization of Data and AI in Plant Engineering | The presentation will introduce how data and AI are being utilized in the plant engineering business to solve business issues through case studies. |
| Class 4 | DXing of Justice, Trends in LegalTech, AI and Law | The presentation will cover legal fundamentals, DXing the judiciary, AI assisting legal work, legal challenges in AI, and more. |
| Class 5 | The future of semiconductor manufacturing equipment created by AI and data science. | Contribution and utilization of AI and DX in the semiconductor industry, acquiring knowledge of manufacturing processes |
| Class 6 | From Basics to Generative AI: An In-depth Exploration （Lecture in English) | Students will gain foundational knowledge of AI and Data Science, understand Generative AI principles, applications, ethical implications, and emerging trends, preparing them for real-world AI challenges and opportunities. We will also introduce JERA’s efforts in the field of AI. |
| Class 7 | Artificial Intelligence and Data Science Advancing Corporate DX | After sharing the state of utilization of Artificial Intelligence and Data Science in the private sector, we will introduce how think tanks and consulting firms are using Artificial Intelligence and Data Science to provide services to promote DX in their client companies, based on a case study of Mitsubishi Research Institute, Inc. We will also introduce our own DX initiatives. |
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.)
Only students of doctor curse are acceptable. Other students must take DSA.P411 " Applied Practical Data Science and AI 1A" 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 C1 (XCO.T689-1), which was offered until FY2023. Students who have taken Progressive Applied AI and Data Science C1 may not take this course.
