2022 Applied Artificial Intelligence and Data Science A

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Academic unit or major
School of Computing
Kanezaki Asako  Miyake Yoshihiro  Ono Isao  Nitta Katsumi  Nagahashi Hiroshi  Kobayashi Takao  Okoshi Taku  Hirate Yu  Akashika Hideki    Takahashi Tsubasa    Yamamoto Koji 
Class Format
Lecture    (Livestream)
Media-enhanced courses
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Offered quarter
Syllabus updated
Lecture notes updated
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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.

Course schedule/Required learning

  Course schedule Required learning
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.

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 T2SCHOLA in advance and shared in the Zoom lecture

Assessment criteria and methods

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 (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 "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.


This lecture is supported by Rakuten, Yahoo Japan Corporation, LINE Inc. and Google LLC.

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