2021 Faculty Courses School of Computing Major courses
Advanced Artificial Intelligence and Data Science C
- Academic unit or major
- Major courses
- Instructor(s)
- Asako Kanezaki / Yoshihiro Miyake / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Toshiyuki Tsurumi / Akiko Sato / Tetsuya Tamura / Takeshi Yamada / Shotaro Fujimoto / Kidai Hayashi / Seiya Yoshimoto / Daishiro Nishida / Yuya Nakano / Hayato Tomita
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 9-10 Tue
- Class
- -
- Course Code
- XCO.T485
- Number of credits
- 100
- Course offered
- 2021
- Offered quarter
- 1Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
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.
Course description and aims
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.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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 |
Study advice (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
Evaluation methods and criteria
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
none
Other
This lecture is supported by Nefrock Inc. and TeamLab Inc..