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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)
- Kanezaki Asako Miyake Yoshihiro Nitta Katsumi Nagahashi Hiroshi Kobayashi Takao Tsurumi Toshiyuki Sato Akiko Tamura Tetsuya Yamada Takeshi Fujimoto Shotaro Hayashi Kidai Yoshimoto Seiya Nishida Daishiro Nakano Yuya Tomita Hayato
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue9-10()
- Group
- -
- Course number
- XCO.T485
- Credits
- 1
- Academic year
- 2021
- Offered quarter
- 1Q
- Syllabus updated
- 2021/3/19
- 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 Nefrock Inc. and Team Lab Inc. about application of AI and Data Science to solve practical problems. |
Keywords
artificial intelligence, data science, digital art
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 | 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 2 | Planning Approach for AI Development (1) | To understand the viewpoints necessary for AI development through a user-driven approach technology required for autonomous driving. |
| Class 3 | Planning Approach for AI Development (2) | To understand the viewpoints necessary for AI development through a user-driven approach . |
| 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 for virtual digital arts. |
| Class 7 | Usage of artificial intelligence for digital art (3) | To understand the production process of interaction mechanism using the deep learning |
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 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 Nefrock Inc. and TeamLab Inc..
