機器學習入門與應用
Machine Learning: Foundations and Applications
| 節 | 週一 |
|---|---|
5 13:20–14:10 | 機器學習入門與應用 CS-PC3 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
This course introduces the concepts and implementations of the most important machine learning approaches used in data analysis and prediction, covering both mathematical theorems and practical applications. All machine learning models will be described with several worked examples and case studies. This course also introduces the basic concepts of artificial neural networks and deep learning. * All lectures take place in the classroom, not online. * 實體上課，無線上上課。
Basic Python programming skills.
homework: 50% final project: 50%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction |
| 第 2 週 | Simple machine learning |
| 第 3 週 | Artificial neural networks |
| 第 4 週 | Evaluation |
| 第 5 週 | Regression Models |
| 第 6 週 | Regression Models |
| 第 7 週 | 清明節 |
| 第 8 週 | Similarity-based learning |
| 第 9 週 | Probability-based Learning |
| 第 10 週 | Information-based learning |
| 第 11 週 | SVM |
| 第 12 週 | Unsupervised learning |
| 第 13 週 | Unsupervised learning |
| 第 14 週 | Generative models & Transformers |
| 第 15 週 | Final projection presentation |
| 第 16 週 | Final projection presentation |
[1] John D. Kelleher, Brian Mac Namee and Aoife D'Arcy, Fundamentals of Machine Learning for Predictive Data Analytics, 2nd, MIT Press, 2020. [2] Aurélien Géron. Hands-On Machine Learning with Scikit-Learn and PyTorch. O'Reilly, 2022.
- 地點
- CS 331
- 時間
- By appointment
- 聯絡方式
- jameschengcs@nycu.edu.tw