2023 Faculty Courses School of Computing Major courses
Practical Artificial Intelligence and Data Science A
- Academic unit or major
- Major courses
- Instructor(s)
- Tsuyoshi Murata / Asako Kanezaki / Norio Tomii / Kei Miyazaki / Keiji Okumura / Jun Sakuma / Katsumi Nitta / Isao Ono / Takao Kobayashi / Yoshihiro Miyake / Yoshiyuki Kobayashi / Takao Saito / Toru Saito / Genya Ogawa / Toshisada Mariyama
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Fri
- Class
- -
- Course Code
- XCO.T493
- Number of credits
- 100
- Course offered
- 2023
- Offered quarter
- 1Q
- Syllabus updated
- Jul 8, 2025
- Language
- Japanese
Syllabus
Course overview and goals
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.
Course description and aims
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.
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)
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).
Keywords
Data Science, Artificial Intelligence, Deep Learning, Materials, IT, Automotive, Electrical Equipment, Law
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Class1－Class7： Lectures
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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 |
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
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and shared in the Zoom lecture
Evaluation methods and criteria
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
None
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
Other
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.