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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 Nagahashi Hiroshi Kobayashi Takao Okoshi Taku Hirate Yu Fernando De Araujo Paulo Takahashi Tsubasa Kazawa Hideto
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8()
- Group
- -
- Course number
- XCO.T483
- Credits
- 1
- Academic year
- 2021
- Offered quarter
- 3Q
- Syllabus updated
- 2021/9/17
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
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.
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 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 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 submit a summary report after each class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| 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 l |
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 the Zoom lecture
Assessment criteria and methods
Summary-sheets at the end of each class 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 (i.e., required knowledge, skills, courses, etc.)
Students of the doctor course are required to register XCOT.687 "Progressive applied artificial intelligence and data science A" instead of XCOT.T483"Applied artificial intelligence and data science A."
Other
This lecture is supported by Rakuten, Yahoo Japan Corporation and Google LLC.
