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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 Nitta Katsumi Nagahashi Hiroshi Kobayashi Takao Nakagawa Kei Takigawa Takayuki Kawamoto Fumio Suimon Yoshiyuki Sugimoto Takashi
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue9-10()
- Group
- -
- Course number
- XCO.T690
- Credits
- 1
- Academic year
- 2021
- Offered quarter
- 4Q
- Syllabus updated
- 2021/10/5
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course is designed to understand
Student learning outcomes
This course aims to develop ability of each student to be more successful in the real world with the consideration of artificial intelligence and data science.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| This lecture is given by cooperate scientists or engineers. |
Keywords
Data Science, Algorithms, Machine Learning, Explainable AI, Economic Statistics, Economic Dynamics Analysis, Alternative Data Analysis, Stock Price Prediction, Time Series Analysis, Cross-Section Analysis, Multi-Factor Model, Portfolio Optimization, Data Model, Data Virtualization, Data Lake, Data Architecture in a Financial Institution, MLOps, KPI Setting
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Online lecture via Zoom.
It is required to submit a report after each class and a total report after the final class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Technology Development and Prospects related to Artificial Intelligence / Big Data required for the Automobile Industry ・The future of connected cars and autonomous driving Do the following in the class ① Suggest new service ideas | ・Understanding the Technology and Future Prospects ・Understand the services and the technology required for autonomous driving. |
| Class 2 | The same as the 1st class | The same as the 1st class |
| Class 3 | AI and data science for finance #1: Utilization of machine learning and alternative data in economic analysis | To understand the perspective of economic statistics necessary for economic analysis of Japan and also understand several use cases of machine learning methods and alternative data analysis methods useful for conducting advanced analysis of economic dynamics. |
| Class 4 | AI and data science for finance #2: Financial time-series analysis | To understand development cases of a time series analysis for predicting future stock prices from past time series data |
| Class 5 | AI and data science for finance #3: Cross-section analysis | To understand a development cases of a cross-section analysis in which a time axis is fixed at a certain point in time and stock prices are predicted from the relationship between various indicators at the base time and future stock prices. |
| Class 6 | AI and data science for finance #4: Portfolio optimization | To understand a development cases of portfolio optimization to automatically select investment targets from multiple investment candidates and optimize each investment weight. |
| Class 7 | AI and data science for finance #5: Development of data infrastructure | To understand advanced technologies related to data utilization infrastructure. |
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 OCW-i in advance and projected in the class room
Japanese textbooks mentioned above
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 Advanced Artificial Intelligence and Data Science A
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Only students of doctor curse are acceptable.
Other students are required to register XCOT.T486 "Advanced Artificial Intelligence and Data Science A" instead of this course.
Other
This lecture is supported by Toyota inc. and Nomura HD inc.
