2020 Advanced Artificial Intelligence and Data Science D

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Academic unit or major
School of Computing
Instructor(s)
Miyake Yoshihiro  Nitta Katsumi  Kanezaki Asako  Tsurumi Toshiyuki  Sato Akiko  Kawamoto Fumio  Nakagawa Kei  Takigawa Takayuki 
Course component(s)
Lecture
Mode of instruction
 
Day/Period(Room No.)
Fri9-10(Zoom)  
Group
-
Course number
XCO.T486
Credits
1
Academic year
2020
Offered quarter
4Q
Syllabus updated
2020/11/23
Lecture notes updated
-
Language used
Japanese
Access Index

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.

Keywords

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 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

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 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.)

none

Other

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

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