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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)
- Miyake Yoshihiro Shudo Kazuyuki Kise Kenji Nitta Katsumi Tsurumi Toshiyuki Sato Akiko Kawamoto Fumio Nakagawa Kei Takigawa Takayuki
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Fri9-10(W531（レクチャーシアター），G115)
- Group
- -
- Course number
- XCO.T486
- Credits
- 1
- Academic year
- 2019
- Offered quarter
- 4Q
- Syllabus updated
- 2019/12/2
- Lecture notes updated
- 2020/2/3
- Language used
- Japanese
- Access Index
-
Course description and aims
This course is designed for students to understand the outline of artificial intelligence development in business and artificial intelligence and data science in the financial industry to consider the possibility to utilize artificial intelligence and data science in the field.
The lecturers will explain broad pictures and recent trends of the topic in each class, as shown below.
Student learning outcomes
This course aims to develop ability of each student to be more successful in the real world with the consideration of artificial intelligence and data science, and also through the opportunity for students to describe their own ideas.
Keywords
artificial intelligence, data science, AI business, user experience, FinTech, financial industry, stock price forecasting
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
This course requires students to take an active role in their own learning. It is required to attend each class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction, AI development in business | To understand the background and purpose of this course, and the basic concepts of AI business. |
| Class 2 | AI development from planning through case studies | To understand the viewpoints necessary for AI development through a series of examples from planning to user use. |
| Class 3 | User experience for AI development (1) | To diversify AI business ideas from the user's point of view and understand the perspectives necessary for AI development. |
| Class 4 | User experience for AI development (2) | To converge the diversified ideas and understand the perspectives required for AI development. |
| Class 5 | Introduction to AI and data science in the financial industry | To understand the background, purpose, and basic concepts of asset management. |
| Class 6 | AI technology in the financial industry | To understand development examples for stock price forecasting. |
| Class 7 | Data science technology in the financial industry | To understand development examples of combination optimization of investment targets. |
| Class 8 | AI and data science utilization platform in the financial industry | To understand advanced technologies related to data utilization infrastructure. |
Textbook(s)
None required
Reference books, course materials, etc.
Materials will be provided on OCW-i in advance and projected in the class room
Assessment criteria and methods
Based on quizzes evaluating students' understanding at the end of each class.
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.T485 ： Advanced Artificial Intelligence and Data Science C
Prerequisites (i.e., required knowledge, skills, courses, etc.)
none
Other
This lecture is supported by Nefrock Inc., and Nomura Holdings, Inc.
