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.
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.
✔ Applicable | How instructors' work experience benefits the course |
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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. |
WEB media, data utilization, information retrieval, big data, machine learning, natural language processing, authentication technology, database, distributed processing, advertising technology, artificial intelligence, data science
✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | Practical and/or problem-solving skills |
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 | Required learning | |
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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 |
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.
None required
Materials will be provided on OCW-i in advance and shared in the Zoom lecture
Summary-sheets at the end of each class and a total report will be considered
Only students of doctor course are acceptable.
It is not allowed to register XCO.T687 and XCO.T483 at once.
This lecture is supported by Rakuten, Yahoo Japan Corporation, LINE and Google LLC.