機器學習
Machine Learning
| 節 | 週三 |
|---|---|
2 09:00–09:50 | 機器學習 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the data observed. This course introduces the basics of learning theories, the design and analysis of learning algorithms, and some applications of machine learning. The course is designed to prepare junior graduate students with a solid background of machine learning and allow them to use machine learning techniques appropriately in their future research or industry projects.
Computer Programming, Calculus, Probability, Linear Algebra
無備註
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● 50% 作業 ● 20% 考試 ● 30% 專題(tentative)
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| 週次 | 主題 |
|---|---|
| 第 1 週 | course introduction; topic 1: when can machines learn? the learning problem |
| 第 2 週 | learning to answer yes/no; types of learning |
| 第 3 週 | feasibility of learning; topic 2: why can machines learn? training versus testing |
| 第 4 週 | the VC dimension; noise and error |
| 第 5 週 | topic 3: how can machines learn? linear regression; logistic regression |
| 第 6 週 | linear models for classification; nonlinear transformation |
| 第 7 週 | topic 4: how can machines learn better? hazard of overfitting; regularization |
| 第 8 週 | validation; three learning principles |
| 第 9 週 | topic 5: how can machines learn by embedding numerous features? linear support vector machine; dual support vector machine |
| 第 10 週 | kernel support vector machine; soft-margin support vector machine |
| 第 11 週 | topic 6: how can machines learn by combining predictive features? blending and bagging; adaptive boosting |
| 第 12 週 | decision tree; random forest; gradient boosted decision tree |
| 第 13 週 | no class as instructor needs to attend ACML 2026 and NeurIPS 2026; recording: machine learning for modern artificial intelligence |
| 第 14 週 | Final exam |
| 第 15 週 | topic 7: how can machines learn by distilling hidden features? neural network; (preliminary) deep learning |
| 第 16 週 | modern deep learning/finale |
Learning from Data, by Yaser Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin
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