2021 (Progressive Applied Artificial Intelligence and Data Science D)

Font size  SML

Register update notification mail Add to favorite lecture list
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.)
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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.


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.



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.


This lecture is supported by Toyota inc. and Nomura HD inc.

Page Top