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 M-b09（光復） 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 aims at training students with common machine learning techniques: clustering, dimensionality reduction (principle component analysis), regression, and binary classification (logistic regression, decision tree, random forest, boosting, support vector machine, and neural network). Specifically, portfolio management and credit default prediction will be covered as applications in FinTech. The connection between machine learning techniques and statistics will be discussed. Examples will be demonstrated in Python. Prof. Henry Horng-Shing Lu will introduce deep neural network and its applications, and Prof. Fang-Pang Lin will cover topics in computation acceleration, reinforcement learning, blockchain and bitcoin. Real market data about stock and future prices will be used (downloaded from open source, or database such as TEJ and Option matrix) will be used as illustration.
Students are expected to be familiar with linear algebra, calculus, and mathematical statistics.
無備註
TA: 高季伶 Email: happy912122@gmail.com Course announcements and materials will be posted in new E3.
1. Tasks: Homework will be assigned mostly weekly base. No late homework will be accepted. TA will post homework solutions for reference in E3. 2. Exam: One exam (in class and closed-book) will be given. 3. Grading policy Participation (10%) Homework (30%) Exams (30%) Project (30%)
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Overview on machine learning and FinTech |
| 第 2 週 | Introduction to Python and exploratory data analysis |
| 第 3 週 | K-means clustering |
| 第 4 週 | Hierarchical clustering and portfolio management. |
| 第 5 週 | Dimension reduction, principle component analysis |
| 第 6 週 | Regression |
| 第 7 週 | Binary classification, confusion matrix Logistic regression. |
| 第 8 週 | Decision trees, support vector machine, neural network |
| 第 9 週 | Exam |
| 第 10 週 | Prof. Lu: Deep learning (1): principles |
| 第 11 週 | Prof. Lu: Deep learning (2): applications |
| 第 12 週 | Prof. Lin: Blockchain & Bitcoin |
| 第 13 週 | Prof. Lin: Practical Computation Acceleration in Finance Calculations |
| 第 14 週 | Prof. Lin: Introduction of Reinforcement Learning in Finance. |
| 第 15 週 | Project presentations |
| 第 16 週 | Project presentations |
| 第 17 週 | Discussions |
| 第 18 週 | Discussions |
The following reference textbooks can be freely download from the NCTU library: James, Witten, Hastie, Tibshirani (2013) An Introduction to Statistical Learning with applications in R. Springer. Hastie, Tibshirani, and Freidman (2009) The Elements of Statistical Learning. Springer
- 地點
- M415
- 時間
- 2EF and appointment
- 聯絡方式
- venteng@gmail.com