2022 Faculty Courses School of Computing Major courses
Applied Artificial Intelligence and Data Science C 1
- Academic unit or major
- Major courses
- Instructor(s)
- Yoshihiro Miyake / Asako Kanezaki / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Naoki Nishimura / Shusaku Yoshizumi / Takayuki Takigawa / Fumio Kawamoto / Yoshiyuki Suimon / Kei Nakagawa
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 9-10 Tue
- Class
- 1
- Course Code
- XCO.T485
- Number of credits
- 100
- Course offered
- 2022
- Offered quarter
- 1Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
The goal of this course is to learn the frontiers of social implementation in artificial intelligence and data science.
The course is given by two classes (Class 1: given in Japanese, Class 2: given in English), and as shown in the lesson plan, overviews of the topic and recent trends are given by lecturers from companies.
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 social implementation of artificial intelligence and data science.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
Lectures of class 1 are given by scientists and engineers of Recruit Inc. and Nomura HD Inc., and lectures of class 2 are given by scientists and engineers of Nomura HD Inc. , Rakuten Group Inc. and Daiichi-Sankyo Inc., about application of AI and Data Science to solve practical problems.
Keywords
artificial intelligence, data science, machine learning, workshop, economic assessment
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 | Business Application Workshop on Machine Learning and Data Utilization (1) | Introduction of data science technology use cases and workshop using Google Colaboratory (1) |
| Class 2 | Business Application Workshop on Machine Learning and Data Utilization (2) | Introduction of data science technology use cases and workshop using Google Colaboratory (2) |
| Class 3 | AI and Data Science in Finance(1) Utilization of machine learning and alternative data in economic analysis AI and data science in the financial field | Understand the view of economic statistics necessary for analysis of the Japanese economy and examples of machine learning and alternative data analysis methods useful for conducting advanced analysis on economic dynamics |
| Class 4 | AI and Data Science in Finance(2) Financial time series analysis | Understand the development case of a time series analysis that predicts future stock prices from past time series data. Understand the development case of a time series analysis that predicts future stock prices from past time series data. |
| Class 5 | AI and Data Science in Finance(3) Cross-section analysis | Understand the development case of a cross-sectional analysis that fixes the time axis to a point in time and predicts stock prices based on the relationship between various indicators and future stock prices at that time |
| Class 6 | AI and Data Science in Finance(4) Portfolio Optimization | Understand portfolio optimization development cases in which multiple investment candidates select investment targets and optimize investment ratios. |
| Class 7 | AI and Data Science in Finance(5) Data infrastructure development | Understand the data and operational infrastructure for large customers. |
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.
Textbook(s)
None required
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and shared in Zoom lecture
Evaluation methods and criteria
Based on quizzes 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.T486 ： Advanced Artificial Intelligence and Data Science D
Prerequisites
Students of the doctor course are required to register XCOT.69-1 "Progressive Artificial Intelligence and Data Science C-1."
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Katsumi Nitta nitta.k.aa[at]m.titech.ac.jp
Asako kanezakii kanezaki[at]c.titech.ac.jp
Office hours
Contact by e-mail in advance to schedule an appointment.
Other
This course is supported by Recruit Inc. and Nomura Holdings Inc..