2021 Advanced Artificial Intelligence and Data Science D

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Academic unit or major
School of Computing
Miyake Yoshihiro  Nitta Katsumi  Nagahashi Hiroshi  Kobayashi Takao  Nakagawa Kei  Takigawa Takayuki  Kawamoto Fumio  Suimon Yoshiyuki  Sugimoto Takashi 
Class Format
Media-enhanced courses
Day/Period(Room No.)
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Academic year
Offered quarter
Syllabus updated
Lecture notes updated
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Course description and aims

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.

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, and also through the opportunity for students to describe their own ideas.

Course taught by instructors with work experience

Applicable How instructors' work experience benefits the course
This lecture is given by cooperate scientists or engineers about application of AI and Data Science to the practical systems.


artificial intelligence, data science, AI business, user experience, FinTech, financial industry, stock price forecasting

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 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 Same as the 1st class 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.


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

Assessment criteria and methods

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 (i.e., required knowledge, skills, courses, etc.)

Students of the doctor course are required to register XCO.T690 "Progressive Applied Artificial Intelligence and Data Science D" instead of XCO.T486.


This lecture is supported by Toyota Inc., and Nomura Holdings, Inc.
Online lecture using Zoom.

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