“2016 Summer Seminar for Machine learning”版本间的差异
来自cslt Wiki
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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://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] | | 2016/07/04 ||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] | ||
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− | | 2016/07/05 ||Dong Wang || Linear models || | + | | 2016/07/05 ||Dong Wang || Linear models || || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt2.%20Linear%20Model.pptx slides][http://arch.cslt.org/video/2016/sum-ML/lesson2-1.mp4 video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson2-2.mp4 video(part 2)] [http://cs229.stanford.edu/notes/cs229-notes1.pdf NG's lecture 1] [http://cs229.stanford.edu/notes/cs229-notes2.pdf NG's lecture 2] |
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− | | 2016/07/08 ||Dong Wang || Neural networks || | + | | 2016/07/08 ||Dong Wang || Neural networks || || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt3.%20Neural%20Network.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson3-1.mp4 video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson3-2.mp4 video(part 2)] |
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− | | 2016/07/11 ||Dong Wang || Deep learning (1)|| | + | | 2016/07/11 ||Dong Wang || Deep learning (1)|| || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt4.%20Deep%20learning-1.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson4-1_Deep_learning.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson4-2_Deep_learning.m4v video(part 2)] [http://www.iro.umontreal.ca/~bengioy/talks/DL-Tutorial-NIPS2015.pdf NIPS 2015 tutorial] |
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− | | 2016/07/12 ||Dong Wang || Deep learning (2)|| | + | | 2016/07/12 ||Dong Wang || Deep learning (2)|| || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt5.%20Deep%20learning-2.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson5-1_Deep_learning.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson5-2_Deep_learning.m4v video(part 2)] [http://www.icassp2016.org/SP16_PlenaryDeng_Slides.pdf Li Deng's ICASSP16 keynote] |
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− | | 2016/07/13 ||Caixia Wang || Kernel methods || | + | | 2016/07/13 ||Caixia Wang || Kernel methods || || [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/0/07/KM1.pdf slides] [http://arch.cslt.org/video/2016/sum-ML/lesson6_Kernel_method.m4v video] [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b5/Kernel_Methods_for_Pattern_Analysis.pdf Kernel method book] [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/95/Pattern_Recognition_and_Machine_Learning.pdf pattern recognition 6-7] |
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− | | 2016/07/18 ||Yang Feng || Graphical model (1) || | + | | 2016/07/18 ||Yang Feng || Graphical model (1) || || [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/d/dd/Chpt7._Graphical_models-bayesian_approach.pdf slides] [http://arch.cslt.org/video/2016/sum-ML/lesson7_Graphical_model.m4v video] |
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− | | 2016/07/21 ||Dong Wang || Graphical model (2) || | + | | 2016/07/21 ||Dong Wang || Graphical model (2) || ||[http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt%208.%20Graphical%20models-2.pptx slides] [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chapt8/Graphical%20models-learningInference.pptx Yang's slides] [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chapt8/GraphicalModel_Jordan.pdf Jordan's lecture] |
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− | | 2016/07/25 ||Dong Wang || Unsupervised learning || | + | | 2016/07/25 ||Dong Wang || Unsupervised learning || || |
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− | | 2016/07/26 ||Dong Wang || Non parametric models || | + | | 2016/07/26 ||Dong Wang || Non parametric models || || [http://cs229.stanford.edu/section/cs229-gaussian_processes.pdf Gaussian process] |
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− | | 2016/07/27 ||Dong Wang || Reinforcement learning || | + | | 2016/07/27 ||Dong Wang || Reinforcement learning || || |
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| 2016/07/28 ||Maoning Wang || Evolutionary learning || || | | 2016/07/28 ||Maoning Wang || Evolutionary learning || || | ||
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− | | 2016/07/29 ||Dong Wang || Optimization || | + | | 2016/07/29 ||Dong Wang || Optimization || || [http://cs229.stanford.edu/section/cs229-cvxopt.pdf Convex optimization I] [http://cs229.stanford.edu/section/cs229-cvxopt.pdf Convex optimization II] |
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2016年7月22日 (五) 06:11的版本
- Location: FIT-1-304
Date | Speaker | Title | Owner | 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 | slidesvideo(part 1) video(part 2) NG's lecture 1 NG's lecture 2 | |
2016/07/08 | Dong Wang | Neural networks | slides video(part 1) video(part 2) | |
2016/07/11 | Dong Wang | Deep learning (1) | slides video(part 1) video(part 2) NIPS 2015 tutorial | |
2016/07/12 | Dong Wang | Deep learning (2) | slides video(part 1) video(part 2) Li Deng's ICASSP16 keynote | |
2016/07/13 | Caixia Wang | Kernel methods | slides video Kernel method book pattern recognition 6-7 | |
2016/07/18 | Yang Feng | Graphical model (1) | slides video | |
2016/07/21 | Dong Wang | Graphical model (2) | slides Yang's slides Jordan's lecture | |
2016/07/25 | Dong Wang | Unsupervised learning | ||
2016/07/26 | Dong Wang | Non parametric models | Gaussian process | |
2016/07/27 | Dong Wang | Reinforcement learning | ||
2016/07/28 | Maoning Wang | Evolutionary learning | ||
2016/07/29 | Dong Wang | Optimization | Convex optimization I Convex optimization II |