機器學習概論
Introduction to Machine Learning
| 節 | 週三 |
|---|---|
5 13:20–14:10 | 機器學習概論 CS 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 described with several worked examples and case studies. This course also introduces the basic concepts of artificial neural networks and deep learning.
Linear algebra, statistics, probability, and computer programming
7-9 homework: 70% Term projects: 30%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction |
| 第 2 週 | Environment setting |
| 第 3 週 | Data analysis |
| 第 4 週 | Probability-based Learning |
| 第 5 週 | Probability-based Learning |
| 第 6 週 | Information-based Learning |
| 第 7 週 | Information-based Learning |
| 第 8 週 | Similarity-based Learning |
| 第 9 週 | Similarity-based Learning |
| 第 10 週 | Unsupervised learning |
| 第 11 週 | Unsupervised learning |
| 第 12 週 | Error-based Learning |
| 第 13 週 | Error-based learning |
| 第 14 週 | Error-based learning |
| 第 15 週 | Artificial neural networks |
| 第 16 週 | Artificial neural networks |
| 第 17 週 | Final project demonstration |
| 第 18 週 | Final project demonstration |
[1] John D. Kelleher, Brian Mac Namee and Aoife D'Arcy, "Fundamentals of Machine Learning for Predictive Data Analytics," MIT Press, 2015. [2] Aurelien Geron, "Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems," O'Reilly Media, 2017.
- 地點
- CS331
- 時間
- 3CD
- 聯絡方式
- jameschengcs@nctu.edu.tw