機器學習與金融科技

This course offers an introduction to machine learning from a statistical perspective, with a strong emphasis on applications in Financial Technology (FinTech), including credit risk modeling, wealth management, and fraud detection. Students will engage in hands-on projects—shared via GitHub—that integrate theoretical concepts with practical programming. The course centers on solving real-world problems in FinTech, encouraging collaboration, innovation, and data-driven decision-making. In addition to technical skills, students will enhance their oral presentation abilities and learn to leverage tools like ChatGPT to improve their coding and writing. Team projects will focus on key FinTech applications such as credit scoring, fraud detection, factor investing in Taiwan’s stock market, and cryptocurrency trading. To facilitate the course, students are required to: 1) Bring a laptop to every lecture. 2) Create free accounts on the following platforms: GitHub, Overleaf, and Microsoft Teams (using their NYCU accounts).

The course covers machine learning principles from a statistical perspective, focusing on FinTech applications. While calculus, probability, and linear algebra are helpful, they are not required. Python proficiency is recommended but not mandatory.

陳諾恆 (Jason Chan)

* Participation 30% (Course Summary, HW presentation, papers summary, in-class exercises, we will use cold calls during class) * Project 30% (must be in slides and manuscript forms of words limits 2400 in a professional writing style) * Exam 40% (You can bring one page formula sheet)

James et al. (July, 2023) An introduction to Statistical Learning with Applications in Python https://hastie.su.domains/ISLP/ISLP_website.pdf GitHUB: https://github.com/HWTeng-Teaching/202509-ML-FinTech

地點

by appointment

時間

by appointment

聯絡方式

Email: venteng@gmail.com