2020 Faculty Courses School of Computing Major courses
Advanced Artificial Intelligence and Data Science D
- Academic unit or major
- Major courses
- Instructor(s)
- Yoshihiro Miyake / Katsumi Nitta / Asako Kanezaki / Toshiyuki Tsurumi / Akiko Sato / Fumio Kawamoto / Kei Nakagawa / Takayuki Takigawa
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 9-10 Fri (Zoom)
- Class
- -
- Course Code
- XCO.T486
- Number of credits
- 100
- Course offered
- 2020
- Offered quarter
- 4Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course is designed for students to understand the outline of artificial intelligence development in business and artificial intelligence and data science in the financial industry to consider the possibility to utilize artificial intelligence and data science in the field.
The lecturers will explain broad pictures and recent trends of the topic in each class, as shown below.
Course description and aims
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, and also through the opportunity for students to describe their own ideas.
Keywords
artificial intelligence, data science, AI business, user experience, FinTech, financial industry, stock price forecasting
Competencies
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | AI and data science for finance #1: Financial time-series analysis | To understand development cases of a time series analysis for predicting future stock prices from past time series data. |
| Class 2 | AI and data science for finance #2: 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 3 | AI and data science for finance #3: 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 4 | AI and data science for finance #4: Development of data infrastructure | To understand advanced technologies related to data utilization infrastructure. |
| Class 5 | AI development from planning through case studies | To understand the viewpoints necessary for AI development through a series of examples from planning to user use. |
| Class 6 | Planning Approach for AI Development (1) | To understand the viewpoints necessary for AI development through a user-driven approach |
| Class 7 | Planning Approach for AI Development (2) | To understand the viewpoints necessary for AI development through a user-driven approach |
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 required
Reference books, course materials, etc.
Materials will be provided on OCW-i in advance and shared in the Zoom lecture
Japanese textbooks mentioned above
Evaluation methods and criteria
Based on reports evaluating students' understanding at the end of each class.
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.T485 ： Advanced Artificial Intelligence and Data Science C
Prerequisites
none
Other
This lecture is supported by Nefrock Inc., and Nomura Holdings, Inc.
Online lecture using Zoom.