2021 Faculty Courses School of Computing Major courses
Practical Artificial Intelligence and Data Science B 1
- Academic unit or major
- Major courses
- Instructor(s)
- Tsuyoshi Murata / Katsumi Nitta / Takao Kobayashi / Hiroshi Nagahashi / Kaoru Watanabe / Hideaki Nishimoto / Ryo Inokuchi / Akihiro Terada / Katsuaki Morita / Yusuke Morimoto
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- 1
- Course Code
- XCO.T494
- Number of credits
- 100
- Course offered
- 2021
- Offered quarter
- 2Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
The purpose of this course is to understand the current status of social implementation of AI and data science technologies and cutting-edge technologies, and to examine the applicability and challenges of these technologies. Trends and issues in technology and product development in the fields of optimization, life insurance, fintech, manufacturing, heavy industry, etc. will be explained in each class as shown in the course schedule.
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.
Keywords
Data Science, Artificial Intelligence, Optimization, Life Insurance, Fintec, Material, Manufacturing Industry, Heavy Industry
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 | (class 1)Solving Operations Optimization Problems with Data Analysis and AI (1) (Hitachi: Kaoru Watanabe) (class 2)Human Extension (Kyocera Corporation: Osamu Hoshuyama ) | Instructions will be given during the lecture. |
| Class 2 | (class 1)Solving Operations Optimization Problems with Data Analysis and AI (2) (Hitachi: Kaoru Watanabe) (class 2)The Future of Next Generation Computers: Challenges and Possibilities of Quantum Computers (Kyocera Corporation: Hidekazu Oki) | Instructions will be given during the lecture. |
| Class 3 | (class 1)The Use of Data Science in Life Insurance Company (The Dai-ichi Life Insurance Company: Hideaki Nishimoto) (class 2) Data utilization to support manufacturing DX in B to B business (NGK Insulators, Ltd.: Takao Saito) | Instructions will be given during the lecture. |
| Class 4 | (class 1)Financial Data Analytics in Practice (Mizuho–DL Financial Technology: Akira Iguchi) (class 2)The Use of Data Science in the Enterprise (Mitsui Chemicals, Inc.: Takeaki Kakigano ) | Instructions will be given during the lecture. |
| Class 5 | (class 1)Finance and Data Science (MUFG Bank: Yuusuke Morimoto) (class 2)Key Points for Introducing AI Visual Inspection in Small and Medium-Sized Manufacturing Industries (Nippon Telegraph and Telephone East Corporation: Yoshinori Ueno) | Instructions will be given during the lecture. |
| Class 6 | (class 1)AI of Manufacturing (Fanuc Corporation: Akihiro Terada) (class 2)Introduction to Ambient Computing (NS Solutions Corporation: kazuhiro Sasao) | Instructions will be given during the lecture. |
| Class 7 | (class 1)Application of AI in Energy Solution Services (Mitsubishi Heavy Industries: Katsuaki Morita) (class 2)Application of image processing technology using a stereo camera to automated driving (SUBARU Corporation: Toru Saito) | Instructions will be given during the lecture. |
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 short report 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
Both credits of Practical Artificial Intelligence and Data Science B-1 and B-2 cannot be obtained. Priority may be given to students enrolled in the Progressive Graduate Minor in Data Science and Artificial Intelligence.
Other
Slide distribution and report acceptance will be done by T2SCHOLA. For more information, please refer to the following site.
http://www.dsai.titech.ac.jp/jissen.html