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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 Masumoto Hiroshi Koyama Nobuyuki Zhenming Shun Tsuchimoto Takayoshi Oka Toshio Igawa Kousaku Imai Takashi Endo Takayuki Okazaki Yoshitaka Muroi Shunichi Kashitani Atsushi
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Fri9-10()
- Group
- 2
- 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 | Data Science in Pharma Industries | An overview of clinical research that observes and analyzes patient data to control factors affecting health and data science used in drug development will be presented. |
| Class 2 | Challenges and Opportunities in Oncology Drug Development ---A statistical Point of View | Various recent attempts to treat cancer and the role of data science for this purpose are described. |
| Class 3 | DXing the Judiciary and the State of LegalTech | This presentation will introduce the IT-enabled court system in Japan (online trials, digitization of court records, etc.), the trend of new services in legal practice called "LegalTech" (online contracts, contract checking, judgment prediction, etc.), and the use of AI technology in these services. |
| Class 4 | Toppan Printing's Use of Data Science and AI in DX | This lecture aims to understand and master the use of data in the manufacturing industry in addition to how research in academia is introduced into industry through case studies of image/document-based R&D in the printing business and manufacturing data analysis/solution development. |
| Class 5 | Solutions for construction, technologies supporting smart construction (AI, 3D, web development) | Smart Construction is a solution for the construction industry and as of July 2022 has been installed on over 20,000 construction sites. Smart Construction is developing in-house to quickly address customer needs, with particular emphasis on the areas of 3D technology, web development, and statistical machine learning. This lecture will introduce product development from the customer's perspective, the in-house development technologies (3D, WEB, ML) that support it, and Saas Plus a Box in actual business use. |
| Class 6 | Digital Strategies and Data Use by Digital General Contractors | Shimizu Corporation's mid-term digital strategy will be explained at the beginning of the session. Examples of data utilization will be presented and discussed for each of the areas that comprise it: "Manufacturing Digitally," "Providing Digital Spaces and Services," and "Digital Supporting Manufacturing. |
| Class 7 | Practical Application of Data Analysis and its Challenges | Machine learning algorithms have evolved over the years, enabling the efficient development of high-performance data analysis programs. However, there are various issues to be addressed in order to apply them to business sites and keep them running continuously. These issues will be introduced along with case studies. |
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
