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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Kanezaki Asako Tomii Norio Murata Tsuyoshi Ono Isao Nitta Katsumi Kobayashi Takao Miyake Yoshihiro Takeshima Shota Tsunashima Koyori Kosaka Junichi Seo Noriaki Iwashita Yoshinori Takemoto Shimpei Arai Ryosuke Hamaguchi Shun Hashimoto Shuki Ueda Tetsuro Nanri Takuya
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Wed7-8()
- Group
- 2
- Course number
- XCO.T495
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 3Q
- Syllabus updated
- 2023/9/27
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
The purpose of this course is to understand the current status of social implementation of AI and data science technologies and cutting-edge technologies, and to examine the applicability and challenges of these technologies. Trends and issues in technology and product development in the fields of Pharmaceutical, Machine Learning, Data Utilization, New Business Development, etc. will be explained in each class as shown in the course schedule.
Student learning outcomes
The goal of this course is for students to acquire knowledge of AI and data science technologies in various fields, and to gain a broader perspective that will enable them to play an active role in the real world by discussing social applications and explaining new ideas in assignment reports.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| This course will be taught by lecturers from (Class 1) Bridgestone, NGK, Panasonic, Sumitomo Heavy Industries, Fujitsu, and Mitsubishi UFJ Bank, and (Class 2) Idemitsu Kosan, Nippon Steel, Nissan Motor, Sumitomo Corporation, Toyo Engineering, Resonac and DIC, based on their practical experience. |
Keywords
Data Science, Artificial Intelligence, FinTech, Manufacturing, Construction, Machine Learning, Data Utilization, New Business Development
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Class1－Class7： Lectures
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Applications of DS and AI technologies in Nippon Steel Corporation | Digital transformation measures that Nippon Steel Corporation is promoting by utilizing DS and AI technologies |
| Class 2 | Domain Knowledge informed Neural Networks ~Beyond Data Collection~ | Learn the importance of understanding the principles behind the subject through the construction of domain knowledge informed neural networks |
| Class 3 | What is DX required in corporate R&D? | To learn the practical application and practice of data-driven science in corporate R&D |
| Class 4 | DX Strategies for “Sogo Shosha”, General Trading Company, Learning from the Fields— Practical Examples of Data Analysis and AI Utilization. | Understanding DX strategies and use cases of data science and AI in a general trading company. |
| Class 5 | How to use data science in the development of chemical materials | Introduction of Data Science Theory and Examples in Chemical Materials Development |
| Class 6 | Data challenges and approaches for unique projects. | The plant EPC business requires the handling of a wide variety of data, as each individual project is unique. However, the same data has different meanings and conditions depending on the project situation and background. These characteristics make data utilisation and analysis difficult. What approaches and case studies are available to address these challenges? Learn about specific approaches through case studies of data utilisation. |
| Class 7 | Concept of information system strategy in a chemical industry, required skills and attitude of DS/AI personnel | This lecture introduces the concept of information system strategy, required human resources and examples of the use of advanced technology. |
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.
They should do so by referring to textbooks and other course material.
Textbook(s)
none
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and shared in the Zoom lecture
Assessment criteria and methods
Mainly short report required in each class 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
- XCO.T483 ： Advanced Artificial Intelligence and Data Science A
- XCO.T484 ： Advanced Artificial Intelligence and Data Science B
- XCO.T485 ： Advanced Artificial Intelligence and Data Science C
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Both credits of Practical Artificial Intelligence and Data Science C-1 and C-2 cannot be obtained. Priority may be given to students enrolled in the Progressive Graduate Minor in Data Science and Artificial Intelligence.
Other
Slide distribution and report acceptance will be done by T2SCHOLA.
