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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)
- Murata Tsuyoshi Ono Isao Nitta Katsumi Kobayashi Takao Nagahashi Hiroshi Miyake Yoshihiro Hanatsuka Yasushi Mori Tppei Kimura Kazuyuki Okada Masashi Kanai Tsukasa Oida Yoshiaki Morimoto Yusuke
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Fri7-8()
- Group
- 1
- Course number
- XCO.T495
- Credits
- 1
- Academic year
- 2022
- Offered quarter
- 3Q
- Syllabus updated
- 2022/9/13
- 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 |
|---|---|
| In this course, lectures based on practical experience are given by lecturers of (class 1) Bridgestone Co., NGK Spark Plug Co., Panasonic Co., Sumitomo Mitsui Truest Bank Co., Fujitsu Ltd., MUFG Bank Ltd., (class 2) Daichi Sankyo Co., NEC Co., Toppan Inc., Komatsu ltd., and Shimizu Co.. |
Keywords
Data Science, Artificial Intelligence, Pharmaceutical, 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 | Solution business developed by a tire manufacturer that creates value through real x digital | Bridgestone is driving a company-wide digital transformation to transform itself from a traditional manufacturing company to a solutions provider. In this presentation, we will introduce how we are digitizing our entire value chain and how we are fostering a culture within the company that utilizes the data obtained from this process for decision-making and value creation. |
| Class 2 | Analysis of time-series data acquired from IoT tires | With the progress of miniaturization and power saving of electronic devices, it has become possible to obtain real-time data on the usage status of various objects. The automobile is the best example of this trend, and it has become a moving electronic device based on the concept of CASE. Tires, a component of automobiles, are no exception, and data such as air pressure, temperature, and acceleration can be obtained in near real time. In this lecture, we will discuss how to realize algorithms that convert such data into more useful information such as tire wear and load with lower load and less memory. |
| Class 3 | Data Science in Real Business Activities | The presentation will introduce current data-driven corporate activities based on data science and digital technology, including the extraction of firing conditions for ceramics in materials research and development. |
| Class 4 | Machine Learning with Differentiability and Uncertainty and Industrial Applications | Differentiability" and "Uncertainty," which are very important concepts supporting future machine learning technology, will be introduced with the lecturer's research on their fundamentals and applications, and expectations for industrial applications such as robot control and automatic monitoring will be discussed |
| Class 5 | New Trends in SDG/ESG Finance - Science Thinking Required | In recent years, many companies have adopted the SDGs and are focusing on creating social impact. The goal of this lecture is to introduce examples of positive social impact created by integrating data science, data engineering, and business skills, using the SDGs as a subject matter, and to give students a sense of business that utilizes scientific, evidence-based, and rational decision-making skills without being bound by precedent. |
| Class 6 | How to design and proceed with AI application projects | Practical AI application projects require optimal management of various uncertainties. This lecture will introduce several real-world AI application projects and outline the uncertainties and how to deal with them at each stage of project conception, design, and execution. |
| Class 7 | Finance and Data Science | Using currency forecasting as the subject matter, this hands-on course introduces participants to the business of currency trading and the data science used in it. |
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. For more information, please refer to the following site.
https://sites.google.com/view/tokyotechdsai/jissen
