2023 Applied Artificial Intelligence and Data Science A

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Academic unit or major
School of Computing
Kanezaki Asako  Tomii Norio  Miyake Yoshihiro  Ono Isao  Nitta Katsumi  Kobayashi Takao  Koyama Nobuyuki  Aoyagi Kenji    Igawa Kousaku  Tsuchimoto Takayoshi  Okada Takashi  Kanai Tsukasa  Okazaki Yoshitaka  Furukawa Kei  Tanji Hironori 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Day/Period(Room No.)
Course number
Academic year
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 lecture will be given by lecturers from companies such as Daiichi Sankyo, Toppan, Yahoo, Komatsu, Shimizu Corp. , Sumitomo Mitsui Trust Bank.


Data utilization, big data, machine learning, 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 Data science for new drug development in pharmaceutical industry How data science can contribute to new drug development in pharmaceutical industry will be explained in this lecture.
Class 2 The applicability of AI and data science in life science This lecture will discuss how AI and machine learning can contribute to the development of life sciences.
Class 3 Utilization of Data Science and AI in Toppan DX This lecture will focus on the research and development of image/document R&D and manufacturing data analysis/solution development cases in the printing business. We aim to understand data utilization in the manufacturing industry.
Class 4 Data utilization at Yahoo! JAPAN Sharing AI/Data Utilization Cases at Yahoo! JAPAN
Class 5 Creating Customer Value with DX, AI, and IoT: Introduction of Construction Tech Examples Introduce how ICT is used to solve real business problems through construction tech case examples
Class 6 AI and Data Application in the Construction Industry Learn the importance of digitalization through examples of AI and data utilization initiatives.
Class 7 Impact Orientation and Scientific Thinking in ESG Finance Learning from the logical perspective of impact (societal outcomes) and the cutting-edge domain of ESG finance where science and finance merge.

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
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 offered in cooperation with Daiichi Sankyo, Toppan, Yahoo! JAPAN, LINE, Komatsu, and Shimizu Corporation.

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