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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
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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)
- Miyake Yoshihiro Murata Tsuyoshi Nitta Katsumi Nagahashi Hiroshi Kobayashi Takao Watanabe Kaoru Miura Satoru Hamamoto Kenichi Iwakabe Koichi Okada Koichi Saito Toru Ogawa Genya
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Fri9-10()
- Group
- 2
- Course number
- XCO.T494
- Credits
- 1
- Academic year
- 2022
- Offered quarter
- 2Q
- Syllabus updated
- 2022/5/9
- 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 fintech, IT telecommunication, manufacturing, heavy industry, construction 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 |
|---|---|
| In this course, lectures based on practical experience are given by lecturers of (class 1) NS Solutions Inc., Dai-ichi Life Group Inc., Mizuho Financial Group Inc., Furukawa Electric Inc., Fanuc Co., Mitsubishi Heavy Industries Inc., (class 2) Hitachi Ltd., Kajima Co., Mitsui Chemicals Inc., NTT East Co., and SUBARU Co. |
Keywords
Data Science, Artificial Intelligence, Life Insurance, Fintec, Material, Manufacturing Industry, Heavy Industry, Construction
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 | AI and Data Science Solutions for Operations Optimization(1) | One of the areas where AI and data science are most widely used in business is "production management, logistics, inventory control, etc.” In order to optimize operations in these areas, students will learn through mini-exercises (group discussions) and other methods how to accurately understand abstract business requirements and examine them in a form that can be solved by AI and data science. |
| Class 2 | AI and Data Science Solutions for Operations Optimization(2) | One of the areas where AI and data science are most widely used in business is "production management, logistics, inventory control, etc.” In order to optimize operations in these areas, students will learn through mini-exercises (group discussions) and other methods how to accurately understand abstract business requirements and examine them in a form that can be solved by AI and data science. |
| Class 3 | Introduction of Automated Construction System as Construction DX in Civil Engineering Works -Attempts to Convert the Construction Site into a Factory | As one of the solutions to problems such as the shortage and aging of on-site workers, Kajima Corporation is working on the development of A4CSEL (Quad Axel), which has automatic operation of construction equipment and optimization of production design and management at its core, aiming to build a production system that ensures safety while providing a stable and continuous supply of social capital. This lecture will introduce their efforts and consider what is needed to change the conventional way of doing things. |
| Class 4 | Using Data Science in R&D for an Integrated Chemical Company | With the prosperity of data science, companies are also making efforts to utilize data science in their businesses. This lecture will introduce examples of efforts and applications in the research and development of materials. |
| Class 5 | Advance corporate business through AI and data analysis | More than 90% of NTT East's business in Japan is in the corporate market. NTT East serves this market with a sales force of 10,000 people spread across the country. This lecture will explain how AI and data analysis can be used to achieve efficient sales in this vast market. |
| Class 6 | Application of image processing technology using stereo cameras to automatic driving | In order to apply image processing technology in general to automobiles for automatic driving applications, etc., it is necessary to provide detailed responses to a number of rare cases, such as dirt on window panes and weather conditions. This lecture will explain the realistic creation of image processing technology that can be trusted as a product. |
| Class 7 | AI-based image processing technology for driver assistance systems and automated driving systems | This lecture will include examples of the application of image processing using AI/machine learning technology to the automotive field, such as recognition of vehicles and pedestrians ahead, and the challenges involved in practical application, including Subaru's efforts. |
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 B-1 and B-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. For more information, please refer to the following site.
https://sites.google.com/view/tokyotechdsai/jissen
