2021 (Progressive Applied Artificial Intelligence and Data Science A)

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Academic unit or major
School of Computing
Miyake Yoshihiro  Nitta Katsumi  Nagahashi Hiroshi  Kobayashi Takao  Okoshi Taku  Hirate Yu  Fernando De Araujo Paulo    Takahashi Tsubasa    Kazawa Hideto 
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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.


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 and a total report after the final 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

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.


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 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 (i.e., required knowledge, skills, courses, etc.)

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.

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