機器學習概論
Introduction to Machine Learning
| 節 | 週二 |
|---|---|
5 13:20–14:10 | 機器學習概論 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
本課程原則上讓較缺乏機會接觸程式設計的學生優先選修。 第一次上課時老師將手動退選下列選課學生: 資訊學院學生 電機學院學生 並於上課時供非資訊、電機學院的學生優先加選有剩餘名額才供資訊、電機學院學生加選。超過人數上限以抽籤決定。 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. This course is designed for non-EECS students, please do not take this course if you are a student of EE or CS college.
Linear algebra, statistics, probability, and computer programming
4-5 homework: 70% Term projects: 30%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction |
| 第 2 週 | Data analysis and performance evaluation |
| 第 3 週 | Probability-based Learning |
| 第 4 週 | Probability-based Learning |
| 第 5 週 | Information-based Learning |
| 第 6 週 | Information-based Learning |
| 第 7 週 | Similarity-based Learning |
| 第 8 週 | Similarity-based Learning |
| 第 9 週 | Error-based Learning |
| 第 10 週 | Error-based Learning |
| 第 11 週 | Unsupervised learning |
| 第 12 週 | Artificial neural networks |
| 第 13 週 | Artificial neural networks |
| 第 14 週 | Final project demonstration |
| 第 15 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
- 4CD
- 聯絡方式
- jameschengcs@nctu.edu.tw