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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)
- Miyake Yoshihiro Kanezaki Asako Nitta Katsumi Nagahashi Hiroshi Kobayashi Takao Nishimura Naoki Yoshizumi Shusaku Takigawa Takayuki Kawamoto Fumio Suimon Yoshiyuki Nakagawa Kei
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue9-10()
- Group
- 1
- Course number
- XCO.T689
- Credits
- 1
- Academic year
- 2022
- Offered quarter
- 1Q
- Syllabus updated
- 2022/4/4
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
The goal of this course is to learn the forefront of social implementation in artificial intelligence and data science, and to consider the issues of social implementation of one's research.
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
The purpose of this course is to deepen understanding of the social implementation of artificial intelligence and data science, and to enhance students' advanced abilities to play an active role in the real world.
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) Utilization of machine learning and alternative data in economic analysis AI and data science in the financial field | Understand the view of economic statistics necessary for analysis of the Japanese economy and examples of machine learning and alternative data analysis methods useful for conducting advanced analysis on economic dynamics |
| Class 4 | AI and Data Science in Finance(2) Financial time series analysis | Understand the development case of a time series analysis that predicts future stock prices from past time series data. |
| Class 5 | AI and Data Science in Finance(3) Cross-section analysis | Understand the development case of a cross-sectional analysis that fixes the time axis to a point in time and predicts stock prices based on the relationship between various indicators and future stock prices at that time |
| Class 6 | AI and Data Science in Finance(4) Portfolio Optimization | Understand portfolio optimization development cases in which multiple investment candidates select investment targets and optimize investment ratios. |
| Class 7 | AI and Data Science in Finance(5) Data infrastructure development | Understand the data and operational infrastructure for large customers. |
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 Zoom lecture
Assessment criteria and methods
Based on quizzes evaluating students' understanding at the end of each class and a term-end report.
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 ： Applied Artificial Intelligence and Data Science A
- XCO.T486 ： Applied Artificial Intelligence and Data Science D
Prerequisites (i.e., required knowledge, skills, courses, etc.)
This course is intended for doctoral students. For other students, please take Applied AI and Data Science C (XCO.T485-1, XCO.T485-2).
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 kanezaki kanezaki[at]c.titech.ac.jp
Office hours
Contact by e-mail in advance to schedule an appointment.
