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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 Nitta Katsumi Kanezaki Asako Takeuchi Shojiro Kaneshige Yuzo Nishida Daishiro Tomita Hayato Nakano Yuya Yamada Takeshi Hayashi Kidai Fujimoto Shotaro Yoshimoto Seiya Tamura Tetsuya
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue9-10(Zoom)
- Group
- -
- Course number
- XCO.T485
- Credits
- 1
- Academic year
- 2020
- Offered quarter
- 4Q
- Syllabus updated
- 2020/9/18
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course is designed for students to understand the outline of digital art, automobiles, machine translation, and online advertising 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.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| This lectured are given by scientists and engineers of Toyota Corporation and Team Lab Inc. about application of AI and Data Science to solve practical problems. |
Keywords
artificial intelligence, data science, connected cars, autonomous driving, machine translation, online advertising
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 | Technology development and prospects related to artificial intelligence / big data required for the automobile industry | To understand artificial intelligence / big data technology and future prospects necessary to achieve the needs of automobiles required by modern society. |
| Class 2 | The future of connected cars and autonomous driving | To focus on connected and autonomous driving in automotive trends and understand the services that spread from the car which can connect with society and the technology required for autonomous driving. |
| Class 3 | Based on previous two lectures, do the following in the class ① Suggest new service ideas ② Feedback on suggestions | Hold a workshop to propose new ideas about the services and autonomous driving. |
| Class 4 | Introduction to artificial intelligence and data utilization through examples | To understand the possibility of utilization of AI and data based on actual cases. |
| Class 5 | Usage of artificial intelligence for digital art (1) | To understand the overview of work of digital art |
| Class 6 | Usage of artificial intelligence for digital art (2) | To understand the algorithm selection and design process assuming virtual works. |
| Class 7 | Usage of artificial intelligence for digital art (3) | To understand techniques for creating interactions using deep learning.application to machine translation. |
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 required
Reference books, course materials, etc.
Materials will be provided on OCW-i in advance and shared in Zoom lecture
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.T486 ： Advanced Artificial Intelligence and Data Science D
Prerequisites (i.e., required knowledge, skills, courses, etc.)
none
Other
This lecture is supported by teamLab Inc. and Toyota Motor Corporation.
