2023 Practical Artificial Intelligence and Data Science A

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Academic unit or major
School of Computing
Murata Tsuyoshi  Kanezaki Asako  Tomii Norio  Miyazaki Kei  Okumura Keiji  Sakuma Jun  Nitta Katsumi  Ono Isao  Kobayashi Takao  Miyake Yoshihiro  Kobayashi Yoshiyuki  Saito Takao  Saito Toru  Ogawa Genya 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Day/Period(Room No.)
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Academic year
Offered quarter
Syllabus updated
Lecture notes updated
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Course description and aims

The purpose of this course is to understand the current status and state-of-the-art of social implementation of AI and data science technologies, and to examine the applicability and challenges of these technologies. As shown in the lesson plan, in each class, trends and issues in technology and product development in the fields of IT, materials, automobiles, electrical equipment, etc. will be explained.

Student learning outcomes

The goal of this course is for students to acquire knowledge of AI and data science technologies in various fields, and to gain a broader perspective that will enable them to play an active role in the real world by discussing social applications and explaining new ideas in assignment reports.

Course taught by instructors with work experience

Applicable How instructors' work experience benefits the course
The purpose of this lecture is to introduce the practical experience of engineers from companies that are implementing data science and artificial intelligence technologies in society (Sony Corporation, NGK Insulators, Mitsubishi Electric Corporation, and Subaru Corporation).


Data Science, Artificial Intelligence, Deep Learning, Materials, IT, Automotive, Electrical Equipment, Law

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Class1-Class7: Lectures

Course schedule/Required learning

  Course schedule Required learning
Class 1 Promotion and Application of Deep Learning at Sony (1) Learn about the current state of AI from a corporate perspective and examples of companies' initiatives for the AI era
Class 2 Promotion and Application of Deep Learning at Sony (2) Learn about the current state of AI from a corporate perspective and examples of companies' initiatives for the AI era
Class 3 Challenge to data-driven manufacturing Data scientist activity for data utilization in manufacturing field.
Class 4 Industrial application of artificial intelligence technology In this course, practical application examples of artificial intelligence technology will be introduced. Through the understanding of practical examples, students will acquire appropriate selection skills for algorithms according to the task.
Class 5 Application of image processing technology using stereo cameras to automatic driving In order to apply image processing technology in general to automobiles for automatic driving applications, etc., it is necessary to provide detailed responses to a number of rare cases, such as dirt on window panes and weather conditions. This lecture will explain the realistic creation of image processing technology that can be trusted as a product.
Class 6 AI-based image processing technology for driver assistance systems and automated driving systems This lecture will include examples of the application of image processing using AI/machine learning technology to the automotive field, such as recognition of vehicles and pedestrians ahead, and the challenges involved in practical application, including Subaru's efforts.
Class 7 DXing of Justice and Trends in LegalTech Learn about applications of AI and data science in courts and law firms

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.



Reference books, course materials, etc.

Materials will be provided on T2SCHOLA in advance and shared in the Zoom lecture

Assessment criteria and methods

Mainly assignment reports required in 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
  • XCO.T483 : Advanced Artificial Intelligence and Data Science A
  • XCO.T484 : Advanced Artificial Intelligence and Data Science B
  • XCO.T485 : Advanced Artificial Intelligence and Data Science C
  • XCO.T486 : Advanced Artificial Intelligence and Data Science D

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


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


This course is supported by Sony Inc., NGC Insulators Inc., Mitsubishi Electric Inc., AGC Inc., IHI Inc. and Asahi Kasei Inc.

Slide distribution and report acceptance will be done by T2SCHOLA.

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