機器學習概論
Introduction to Machine Learning
| 節 | 週二 | 週五 |
|---|---|---|
2 09:00–09:50 | 機器學習概論 ED117（光復） | |
5 13:20–14:10 | 機器學習概論 ED117（光復） 2 節連堂 | |
6 14:20–15:10 |
* 根據陽明交大上課時間表所列
This course introduces a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
Linear algebra, statistics, probability, and computer programming
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4-6 homework: 50% Term projects: 50%
Introduction
- 講授：
- 9
Information-based Learning
- 講授：
- 3
Similarity-based Learning
- 講授：
- 6
Probability-based Learning
- 講授：
- 12
Error-based Learning
- 講授：
- 9
Evaluation
- 講授：
- 3
Case Study
- 講授：
- 12
| 週次 | 主題 |
|---|---|
| 第 1 週 | Machine Learning for Predictive Data Analytics |
| 第 2 週 | Data to Insights to Decisions |
| 第 3 週 | Data Exploration |
| 第 4 週 | Information-based Learning |
| 第 5 週 | Similarity-based Learning |
| 第 7 週 | Probability-based Learning |
| 第 11 週 | Error-based Learning |
| 第 14 週 | Evaluation |
| 第 15 週 | Case Study |
John D. Kelleher, Brian Mac Namee and Aoife D'Arcy, "Fundamentals of Machine Learning for Predictive Data Analytics," MIT Press, 2015.
- 地點
- EC444 and EC701
- 時間
- By appointment
- 聯絡方式
- ytc@cs.nctu.edu.tw chengchc@cs.nctu.edu.tw