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 includes linear regression, logistic regression, neural networks, machine learning system design and advice, support vector machines, decision tree, boosting, etc. In addition, we will also discuss the benefit of multitask learning and meta-learning. We use microsoft teams with the link https://tinyurl.com/5trweptk
Basics of probability theory, linear algebra, and multivariable calculus Reasonably computer programming skills in Matlab/Python/numpy We use microsoft teams with the link https://tinyurl.com/5trweptk
無備註
Reference web sites: 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 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 https://tinyurl.com/5trweptk 9/15 |
| 第 2 週 | 2. Linear regression with multiple variables https://tinyurl.com/5trweptk 9/22 |
| 第 3 週 | 3. Logistic regression https://tinyurl.com/5trweptk 9/29 |
| 第 4 週 | 4. Neural networks: representation https://tinyurl.com/5trweptk 10/6 |
| 第 5 週 | 5. Neural networks: learning https://tinyurl.com/5trweptk 10/13 |
| 第 6 週 | 6. Machine learning system design and advice https://tinyurl.com/5trweptk 10/20 |
| 第 7 週 | 7. Support Vector Machines https://tinyurl.com/5trweptk 7 10/27 |
| 第 8 週 | 8. Unsupervised learning and dimensionality reduction https://tinyurl.com/5trweptk 11/3 |
| 第 9 週 | 9. Anomaly detection and recommender systems https://tinyurl.com/5trweptk 11/10 |
| 第 10 週 | 10. Large scale machine learning https://tinyurl.com/5trweptk 11/17 |
| 第 11 週 | 11. Application Example: Photo OCR https://tinyurl.com/5trweptk 11/24 |
| 第 12 週 | 12. Recap, Review, and Term project proposal https://tinyurl.com/5trweptk 12/1 |
| 第 13 週 | 13. Statistical learning: Tree-based methods & Boosting Methods & Ensemble methods https://tinyurl.com/5trweptk 12/8 |
| 第 14 週 | 14. Variational Auto Encoder https://tinyurl.com/5trweptk 12/15 |
| 第 15 週 | 15. Meta-Learning https://tinyurl.com/5trweptk 12/22 |
| 第 16 週 | 16. Term project report https://tinyurl.com/5trweptk 12/29 |
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
- 教師未提供此項資料
- 時間
- on appointment
- 聯絡方式
- email: machingwen@ncyu.edu.tw