“2016 Summer Seminar for Machine learning”版本间的差异
来自cslt Wiki
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| 2016/07/ ||Dong Wang || Deep learning (2)|| | | 2016/07/ ||Dong Wang || Deep learning (2)|| | ||
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| + | | 2016/07/ ||Dong Wang || Unsupervised learning || | ||
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| 2016/07/ ||Caixia Wang || Kernel methods || | | 2016/07/ ||Caixia Wang || Kernel methods || | ||
| 第23行： | 第25行： | ||
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| 2016/07/ ||Yang Feng || Graphical model: Random field || | | 2016/07/ ||Yang Feng || Graphical model: Random field || | ||
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| 2016/07/ ||Dong Wang || No parametric models || [http://cs229.stanford.edu/section/cs229-gaussian_processes.pdf Gaussian process] | | 2016/07/ ||Dong Wang || No parametric models || [http://cs229.stanford.edu/section/cs229-gaussian_processes.pdf Gaussian process] | ||
2016年7月3日 (日) 14:01的版本
- Location: FIT-1-304
| Date | Speaker | Title | Materials |
|---|---|---|---|
| 2016/07/04 | Dong Wang | Machine learning overview | Algebra review probability review Gaussian distribution |
| 2016/07/05 | Dong Wang | Linear models | NG's lecture 1 AG's lecture 2 |
| 2016/07/ | Dong Wang | Neural networks | |
| 2016/07/ | Dong Wang | Deep learning (1) | |
| 2016/07/ | Dong Wang | Deep learning (2) | |
| 2016/07/ | Dong Wang | Unsupervised learning | |
| 2016/07/ | Caixia Wang | Kernel methods | |
| 2016/07/ | Dong Wang | Probabilistic learning theory | |
| 2016/07/ | Yang Feng | Graphical model: Bayesian approach | |
| 2016/07/ | Yang Feng | Graphical model: Random field | |
| 2016/07/ | Dong Wang | No parametric models | Gaussian process |
| 2016/07/ | Dong Wang | Reinforcement learning | |
| 2016/07/ | Maoning Wang | Evolutionary learning | |
| 2016/07/ | Dong Wang | Optimization | Convex optimization I Convex optimization II |
