Python 與機器學習演算法
Python and Machine Learning Algorithms
| 節 | 週一 |
|---|---|
2 09:00–09:50 | Python 與機器學習演算法 SC201 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
Machine learning has been shown powerful on many specific problems. While there are many existing packages to perform various machine learning tasks, we must understand the relations between the parameters and the outcome. This course will start with an overview of linear algebra, which is the foundation of many machine learning algorithms. Then you will learn the theory behind each algorithm and how to implement them from scratch (which should be fun!). With these insights, you will be more confident in picking the models, tuning the hyperparameters, and even contriving new algorithms.
Students taking this course are expected to have a basic understanding of linear algebra, including vector spaces and bases, and fundamental Python skills, including writing code with for-loops and if-statements.
Course website: https://jupiter.math.nycu.edu.tw/~chlin/2026SSCMA30084/2026SSCMA30084.html
60% Assignment + 20% LA exam + 20% ML exam
| 週次 | 主題 |
|---|---|
| 第 1 週 | Linear Algebra with NumPy |
| 第 2 週 | Linear Algebra with NumPy |
| 第 3 週 | Linear Algebra with NumPy |
| 第 4 週 | Linear Algebra with NumPy |
| 第 5 週 | Linear Algebra with NumPy |
| 第 6 週 | LA exam |
| 第 7 週 | no class |
| 第 8 週 | PCA |
| 第 9 週 | MDS |
| 第 10 週 | k-means |
| 第 11 週 | DBSCAN |
| 第 12 週 | Linear regression |
| 第 13 週 | no class |
| 第 14 週 | k-nearest neighbors |
| 第 15 週 | Decision tree |
| 第 16 週 | ML exam |
Linear Algebra with NumPy by Jephian Lin Machine Learning with NumPy by Jephian Lin Python Data Science Handbook by Jake VanderPlas, O’Reilly Media, 2016
- 地點
- SA336
- 時間
- Appointments by email
- 聯絡方式
- jephianlin@gmail.com