機器學習
Machine Learning
| 節 | 週三 |
|---|---|
5 13:20–14:10 | 機器學習 CM216（歸仁） 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
Many researchers consider machine learning as a promising technology towards human-level artificial intelligence. Without being explicitly programmed, the computer learns from big data set to do a lot of tasks such as image classification, speech recognition, language translation, autonomous driving, etc. This course, with the assistance of well-known free on-line courses, provides basic and general concepts of machine learning. Topics include linear regression, logistic regression, neural networks, machine learning system design and advice, support vector machines, decision tree, boosting, etc. In addition, we will discuss the benefit of multitask learning and meta-learning.
Basics of probability theory, linear algebra, and multivariable calculus Reasonably computer programming skills in Matlab/Python/numpy
無備註
https://www.coursera.org/learn/machine-learning#about http://faculty.marshall.usc.edu/gareth-james/ISL/
There are two options students can choose from. 1) 10 Homeworks: 1 Midterm test = 66.7%:33.3% 2) 8 Homeworks: 1 Midterm test: 1 Term project = 40%:20%:40% For students who decide to do both ways, they will receive the higher scores from these two grading methods.
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | 1. Course outline, machine learning introduction, and Linear regression with one variable 9/16 |
| 第 2 週 | 2. Linear regression with multiple variables 9/23 |
| 第 3 週 | 3. Logistic regression 9/30 |
| 第 4 週 | 4. Neural networks: representation 10/7 |
| 第 5 週 | 5. Neural networks: learning 10/14 |
| 第 6 週 | 6. Machine learning system design and advice 10/21 |
| 第 7 週 | 7. Support Vector Machines 10/28 |
| 第 8 週 | 8. Unsupervised learning and dimensionality reduction 11/4 |
| 第 9 週 | 9. Anomaly detection and recommender systems 11/11 |
| 第 10 週 | 10. Large scale machine learning 11/18 |
| 第 11 週 | 11. Application Example: Photo OCR 11/25 |
| 第 12 週 | 12. Recap, Review, and Midterm test 12/2 |
| 第 13 週 | 13. Statistical learning: Tree-based methods 12/9 |
| 第 14 週 | 14. Statistical learning: Boosting methods 12/16 |
| 第 15 週 | 15. Multi-task learning: soft-parameter sharing 12/23 |
| 第 16 週 | 16. Multi-task learning: hard-parameter sharing 12/30 |
| 第 17 週 | 17. Guest Lecture: TBD |
| 第 18 週 | 18. Term project report 1/13 |
Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016. Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer Science, 2017
- 地點
- CM517
- 時間
- on appointment
- 聯絡方式
- machingwen@nctu.edu.tw