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（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.
Students are expected to be familiar with linear algebra, calculus, and mathematical statistics.
無備註
TA: 高季伶 Email: happy912122@gmail.com 吳孟芸 Email: jaycars514@gmail.com Course announcements and materials will be posted in new E3.
1. Tasks: Quizzes will be given occasionally. Homework will be assigned mostly weekly base. No late homework will be accepted. TA will post homework solutions for reference in E3. 2. Exam Two exams (in class and closed-book) will be given. 3. Grading policy Participation (5%) Homework (40%) Exams (30%) Project (25%)
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Overview on machine learning and FinTech 9/9 |
| 第 2 週 | Introduction to Python and exploratory data analysis 9/16 |
| 第 3 週 | K-means clustering Student presentation 1: motivations 9/23 |
| 第 4 週 | Hierarchical clustering and portfolio management. 9/30 |
| 第 5 週 | Dimension reduction, principle component analysis Student presentation 1: motivations 10/7 |
| 第 6 週 | Regression Exam 1 10/14 |
| 第 7 週 | Binary classification, confusion matrix Logistic regression. Student presentation 1: motivations 10/21 |
| 第 8 週 | Decision trees, support vector machine, neural network. 10/28 |
| 第 9 週 | Text mining. 11/4 |
| 第 10 週 | Prof. Lu: Deep learning (1): principles 11/11 |
| 第 11 週 | Prof. Lu: Deep learning (2): applications 11/18 |
| 第 12 週 | Student presentation 2: exploratory data analysis 11/25 |
| 第 13 週 | Prof. Lin: Practical Computation Acceleration in Finance Calculations 12/2 |
| 第 14 週 | Prof. Lin: Introduction of Reinforcement Learning in Finance. 12/9 |
| 第 15 週 | Prof. Lin: Blockchain & Bitcoin 12/16 |
| 第 16 週 | Exam 2 12/23 |
| 第 17 週 | Student presentation 3: Project 12/30 |
| 第 18 週 | Discussions 1/6 |
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