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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)
- Nitta Katsumi Tomii Norio Miyazaki Kei Okumura Keiji Sakuma Jun Miyake Yoshihiro Ono Isao Kanezaki Asako Kobayashi Takao Nishimura Naoki Yoshizumi Shusaku Takigawa Takayuki Kawamoto Fumio Suimon Yoshiyuki Nakagawa Kei
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue9-10()
- Group
- 1
- Course number
- XCO.T485
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 1Q
- Syllabus updated
- 2023/3/28
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
The goal of this course is to learn the frontiers of social implementation in artificial intelligence and data science.
The course is given by two classes (Class 1: given in Japanese, Class 2: given in English), and as shown in the lesson plan, overviews of the topic and recent trends are given by lecturers from companies.
Student learning outcomes
This course aims to develop ability of each student to be more successful in the real world with the consideration of social implementation of artificial intelligence and data science.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| Lectures of class 1 are given by scientists and engineers of Recruit Inc. and Nomura HD Inc., and lectures of class 2 are given by scientists and engineers of Nomura HD Inc. , Rakuten Group Inc. and Daiichi-Sankyo Inc., about application of AI and Data Science to solve practical problems. |
Keywords
artificial intelligence, data science, machine learning, workshop, economic assessment
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 attend each class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Business Application Workshop on Machine Learning and Data Utilization (1) | Introduction of data science technology use cases and workshop using Google Colaboratory (1) |
| Class 2 | Business Application Workshop on Machine Learning and Data Utilization (2) | Introduction of data science technology use cases and workshop using Google Colaboratory (2) |
| Class 3 | AI and Data Science in Finance(1) | Understand the application of AI and data science in a Financial Company |
| Class 4 | AI and Data Science in Finance(2) | Understand the application of AI and data science in a Financial Company |
| Class 5 | AI and Data Science in Finance(3) | Understand the application of AI and data science in a Financial Company |
| Class 6 | AI and Data Science in Finance(4) | Understand the application of AI and data science in a Financial Company |
| Class 7 | AI and Data Science in Finance(5) | Understand the application of AI and data science in a Financial Company |
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.
Textbook(s)
None required
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and shared in Zoom lecture
Assessment criteria and methods
No final exam will be given. The evaluation will be based on the reports of each assignment.
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.T486 ： Advanced Artificial Intelligence and Data Science D
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Students of the doctor course are required to register XCOT.69-1 "Progressive Artificial Intelligence and Data Science C-1."
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 kanezakii kanezaki[at]c.titech.ac.jp
Office hours
Contact by e-mail in advance to schedule an appointment.
Other
This course is supported by Recruit Inc. and Nomura Holdings Inc..
