2023 Faculty Courses School of Computing Major courses
Progressive Applied Artificial Intelligence and Data Science C 2
- Academic unit or major
- Major courses
- Instructor(s)
- Asako Kanezaki / Katsumi Nitta / Norio Tomii / Kei Miyazaki / Keiji Okumura / Jun Sakuma / Yoshihiro Miyake / Isao Ono / Takao Kobayashi / Takayuki Takigawa / Lupton Scott / Zhuang Yulin / De Araujo Paulo Fernando / Lu Yong / Ryuji Iwaya / Erick Mendieta / Hoa Le
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Wed
- Class
- 2
- Course Code
- XCO.T689
- Number of credits
- 100
- Course offered
- 2023
- Offered quarter
- 1Q
- Syllabus updated
- Jul 8, 2025
- Language
- English
Syllabus
Course overview and goals
The goal of this course is to learn the forefront of social implementation in artificial intelligence and data science, and to consider the issues of social implementation of one's research.
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
The purpose of this course is to deepen understanding of the social implementation of artificial intelligence and data science, and to enhance students' advanced abilities to play an active role in the real world.
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, investment strategies, e-commerce, clinical development
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 in Finance(1) | Understand the application of AI and data science in Financial Company |
| Class 2 | AI and Data Science in Finance(2) | Understand the application of AI and data science in a Financial Company |
| Class 3 | Tips and Tricks for Building Large Scale Web Services | • Key Concepts About Web Scalability • Internet Business Trends • Common Terminology for Distributed Architectures • Dynamics of Growth • Scalable Design: High Traffic, Distributed Data • How to Prepare Organizations for Growth |
| Class 4 | Data Science and UX/UI Designing | 1) Get ideas on how Data Science will be used in UX field; Web Analytics 2) Through real business cases, get to know how UX approach and Data Science will support each other 3) Through real business cases, get to know how important UI is for state-of-the-art technologies, as well as the other way around 4) With the new knowledge and idea above, widen and deepen your understanding of your own learning field; imagine potential issues in the social installation phase |
| Class 5 | Lessons learned from AI innovation, from research to production. | Show to students what it takes to move a complex AI project to production. - Share common challenges and the different ways AI projects can fail. - Share success stories with the students |
| Class 6 | Apply Data Analytics in Clinical Trial | Data analytics has been a recent trend to enhance Data Sciences capabilities in pharmaceutical industry. The concept, the industry needs, and the available technology supporting the industry application will be discussed. |
| Class 7 | Opportunities and Challenges in the use of Real-World Evidence in the Clinical Development. | Learn that the real-world data (RWD) can be extensively used in the pharmaceutical industry for the product research and development. RWD sources, real-world evidence (RWE), advanced methods including propensity scores and AI/ML, and applications e.g., to inform clinical trial design and to serve as external control arms to support regulatory decision-making for single-arm clinical trials will be discussed. |
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
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 and a term-end report.
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 ： Applied Artificial Intelligence and Data Science A
- XCO.T486 ： Applied Artificial Intelligence and Data Science D
Prerequisites
This course is intended for doctoral students. For other students, please take Applied AI and Data Science C (XCO.T485-1, XCO.T485-2).
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 kanezaki kanezaki[at]c.titech.ac.jp
Office hours
Contact by e-mail in advance to schedule an appointment.