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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Murata Tsuyoshi Nitta Katsumi Kobayashi Takao Nagahashi Hiroshi Kobayashi Yoshiyuki Nakata Takayuki Okada Masashi Tasaki Hirohisa Kitayama Daisuke Kida Takeshi Kawano Yukihiro
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- -
- Group
- -
- Course number
- XCO.T493
- Credits
- 1
- Academic year
- 2021
- Offered quarter
- 1Q
- Syllabus updated
- 2021/4/22
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
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 IT, materials, manufacturing, heavy industry, etc. will be explained in each class as shown in the course schedule.
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.
Keywords
Data Science, Artificial Intelligence, Deep Learning, Machine Learning, Material, Manufacturing Industry, Heavy Industry
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) (Sony Group Corporation: Yuki Kobayashi) | Instructions will be given during the lecture. |
| Class 2 | Promotion and Application of Deep Learning at Sony (2) (Sony Group Corporation: Yuki Kobayashi) | Instructions will be given during the lecture. |
| Class 3 | AI and Data Science in Materials Development and Manufacturing (Asahi Kasei Corporation: Takayuki Nakata) | Instructions will be given during the lecture. |
| Class 4 | Machine Learning with Differentiability and Uncertainty and Industrial Applications (Panasonic Corporation: Masashi Okada) | Instructions will be given during the lecture. |
| Class 5 | Industrial Applications of Artificial Intelligence Technology (Mitsubishi Electric Corporation: Hirohisa Tasaki) | Instructions will be given during the lecture. |
| Class 6 | Examples of Data Science and AI Applications in Manufacturing Industry (AGC Inc: Daisuke Kitayama, Takeshi Kida) | Instructions will be given during the lecture. |
| Class 7 | Application of AI/Data Analysis Technology in Heavy Industries (IHI Corporation: Yukihiro Kawano) | Instructions will be given during the lecture. |
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.
Textbook(s)
none
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and shared in the Zoom lecture
Assessment criteria and methods
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 (i.e., required knowledge, skills, courses, etc.)
Priority may be given to students enrolled in the Progressive Graduate Minor in Data Science and Artificial Intelligence.
Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).
Please see http://www.dsai.titech.ac.jp/jissen.html .
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
