機器學習與金融科技
Machine Learning and FinTech
| 節 | 週一 |
|---|---|
2 09:00–09:50 | 機器學習與金融科技 M102 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
This course provides an introduction to machine learning from a statistical perspective, emphasizing Financial Technology (FinTech) applications such as default prediction and wealth management. Students will apply machine learning techniques through hand-on projects, shared on GitHub, blending theory with practical programming. The course focuses on problem-solving in financial technology, fostering collaboration, innovation, and data-driven decision-making. Students will also develop essential skills in oral presentations and utilize ChatGPT to enhance coding and writing. This dynamic course prepares students for real-world FinTech challenges.
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)
1 Infrastructure 4% 2 Participation 20% 3 Homework 16% 4 Project 30% 5 Exam 30%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Syllabus |
| 第 2 週 | C02: Statistical Learning & Lab on Python |
| 第 3 週 | C12: Unsupervised learning |
| 第 4 週 | C12: Unsupervised Learning |
| 第 5 週 | C12: Unsupervised Learning |
| 第 6 週 | C03: Linear Regression |
| 第 7 週 | C04: Classification |
| 第 8 週 | C04: Classification |
| 第 9 週 | C05: Resampling, C06: Model selection |
| 第 10 週 | C07: Beyond Linearity |
| 第 11 週 | C08: Tree-Based Methods |
| 第 12 週 | C09: SVM, C10: Neural Networks |
| 第 13 週 | Large Language models |
| 第 14 週 | Exam |
| 第 15 週 | Presentation of Projects |
| 第 16 週 | Presentation of Projects |
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/202409-ML-FinTech
- 地點
- by appointment
- 時間
- by appointment
- 聯絡方式
- Email: venteng@gmail.com