“2016 Summer Seminar for Machine learning”版本间的差异
来自cslt Wiki
| 第6行： | 第6行： | ||
! Date !! Speaker!! Title !! Materials | ! Date !! Speaker!! Title !! Materials | ||
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| − | | 2016/07/04 ||Dong Wang || Machine learning overview || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt1.%20Overview%20of%20Machine%20Learning.pptx slides][http://cs229.stanford.edu/section/cs229-linalg.pdf Algebra review] [http://cs229.stanford.edu/section/cs229-prob.pdf probability review] [http://cs229.stanford.edu/section/gaussians.pdf Gaussian distribution][http://cs229.stanford.edu/notes/cs229-notes4.pdf Learning theory] | + | | 2016/07/04 ||Dong Wang || Machine learning overview || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt1.%20Overview%20of%20Machine%20Learning.pptx slides][http://arch.cslt.org/video/2016/sum-ML/lesson1-2.mp4 video(part 2)][http://cs229.stanford.edu/section/cs229-linalg.pdf Algebra review] [http://cs229.stanford.edu/section/cs229-prob.pdf probability review] [http://cs229.stanford.edu/section/gaussians.pdf Gaussian distribution][http://cs229.stanford.edu/notes/cs229-notes4.pdf Learning theory] |
|- | |- | ||
| 2016/07/05 ||Dong Wang || Linear models || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt2.%20Linear%20Model.pptx slides] [http://cs229.stanford.edu/notes/cs229-notes1.pdf NG's lecture 1] [http://cs229.stanford.edu/notes/cs229-notes2.pdf NG's lecture 2] | | 2016/07/05 ||Dong Wang || Linear models || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt2.%20Linear%20Model.pptx slides] [http://cs229.stanford.edu/notes/cs229-notes1.pdf NG's lecture 1] [http://cs229.stanford.edu/notes/cs229-notes2.pdf NG's lecture 2] | ||
2016年7月12日 (二) 04:54的版本
- Location: FIT-1-304
| Date | Speaker | Title | Materials |
|---|---|---|---|
| 2016/07/04 | Dong Wang | Machine learning overview | slidesvideo(part 2)Algebra review probability review Gaussian distributionLearning theory |
| 2016/07/05 | Dong Wang | Linear models | slides NG's lecture 1 NG's lecture 2 |
| 2016/07/08 | Dong Wang | Neural networks | slides |
| 2016/07/11 | Dong Wang | Deep learning (1) | slides NIPS 2015 tutorial |
| 2016/07/ | Dong Wang | Deep learning (2) | |
| 2016/07/ | Dong Wang | Unsupervised learning | |
| 2016/07/ | Caixia Wang | Kernel methods | Kernel method book pattern recognition 6-7 |
| 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 |
