2022 Faculty Courses School of Computing Major courses
Applied Artificial Intelligence and Data Science A
- Academic unit or major
- Major courses
- Instructor(s)
- Asako Kanezaki / Yoshihiro Miyake / Isao Ono / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Taku Okoshi / Yu Hirate / Hideki Akashika / / Tsubasa Takahashi / / Koji Yamamoto
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue
- Class
- -
- Course Code
- XCO.T483
- Number of credits
- 100
- Course offered
- 2022
- 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.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Big Data/AI Applications at Rakuten Group | Introduction to the area of data/AI utilization at Rakuten Group (mainly in the commerce area). We will talk about how much data is collected at Rakuten, how it is utilized for business, how data/AI work is conducted, and what kind of human resources are required. |
| Class 2 | Examples of AI-related R&D at Rakuten Group | Using examples of AI-related R&D at Rakuten Group, we will introduce how corporate R&D organizations contribute to the business. |
| Class 3 | Large-scale web service construction and payment security | The contents to be considered and matters to be careful about when building large-scale web services will be introduced based on case studies, as well as fraud countermeasures and security required for smartphone payments and other services. |
| Class 4 | Data Utilization in Yahoo! JAPAN | Share AI/Data Application Case Studies at Yahoo! JAPAN |
| Class 5 | Cutting-edge AI technology promoted by LINE | The presentation will introduce LINE's vision and business applications in the AI business, as well as examples of LINE's cutting-edge AI technology, including speech and image recognition and synthesis, large-scale language modeling, and research and development on privacy protection and AI reliability. |
| Class 6 | Machine Learning and Data Science Applications in Online Advertising | Presenting examples of the use of machine learning and data science in online advertising |
| Class 7 | Use of data science in input supports for information retrieval | Various search services on the Web have introduced functions to assist search query input for the convenience of search users. This presentation describes efforts to utilize data science to further improve the usability of such functions. |
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 T2SCHOLA in advance and shared in the Zoom lecture
Evaluation methods and criteria
Reports 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
Students of the doctor course are required to register XCOT.687 "Progressive applied artificial intelligence and data science A" instead of XCOT.T483 "Advanced artificial intelligence and data science A."
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Katsumi Nitta nitta.k.aa[at]m.titech.ac.jp
Asako Kanezaki kanezaki[at]c.titech.ac.jp
Hiroshi Nagahashi nagahashi.h.aa[at]m.titech.avcjp
Takao Kobayashi kobayashi.t.aq[at]m.titech.ac.jp
Office hours
Contact by e-mail in advance to schedule an appointment.
Other
This lecture is supported by Rakuten, Yahoo Japan Corporation, LINE Inc. and Google LLC.