2021 Faculty Courses School of Computing Major courses
(Progressive Applied Artificial Intelligence and Data Science A)
- Academic unit or major
- Major courses
- Instructor(s)
- Yoshihiro Miyake / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Taku Okoshi / Yu Hirate / De Araujo Paulo Fernando / / Tsubasa Takahashi / / Hideto Kazawa
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue
- Class
- -
- Course Code
- XCO.T687
- Number of credits
- 100
- Course offered
- 2021
- Offered quarter
- 3Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course is designed for students to understand the outline of WEB media systems focusing on the infrastructure of artificial intelligence and data utilization, information retrieval, and machine learning 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 lectures are given by scientists or engineers from Rakuten, Yahoo, LINE and Google about application of AI and Data Science to the practical systems.
Keywords
WEB media, data utilization, information retrieval, big data, machine learning, natural language processing, authentication technology, database, distributed processing, advertising technology, artificial intelligence, data science
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 submit a summary report after each class and a total report after the final class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | The Potential for AI/Big Data in Business -Based on the case study in Rakuten | To understand the potential for AI/Big Data based on the case of Rakuten |
| Class 2 | (English lecture) Tips and Tricks for Building Large-Scale Web Services | To understand the application of AI technologies to Large-Scale Web services |
| Class 3 | AI related projects at an e-commerce company | To study the application of AI technologies to e-commerce |
| Class 4 | Utilizing data at Yahoo! JAPAN | Share AI/Data Science use cases at Yahoo! JAPAN |
| Class 5 | LINE’ initiatives on R&D and production related to AI | Share AI/Data Science use cases at LINE |
| Class 6 | Machine Translation | Introduction of recent machine translation technologies and applications |
| Class 7 | Online advertising | Applications of machine learning and data science to online advertising |
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 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 the Zoom lecture
Evaluation methods and criteria
Summary-sheets at the end of each class and a total report 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
Prerequisites
Only students of doctor course are acceptable.
It is not allowed to register XCO.T687 and XCO.T483 at once.
Other
This lecture is supported by Rakuten, Yahoo Japan Corporation, LINE and Google LLC.