“2016 Summer Seminar for Machine learning”版本间的差异

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! Date !! Speaker!! Title !!  Owner !! 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 ppt][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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| 2016/07/04  ||Dong Wang  || Machine learning overview || Dong Wang || [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 || [http://cs229.stanford.edu/notes/cs229-notes1.pdf NG's lecture 1] [http://cs229.stanford.edu/notes/cs229-notes2.pdf AG's lecture 2]
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| 2016/07/05  ||Dong Wang  || Linear models || Aiting Liu; Aodong Li|| [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/    ||Dong Wang  || Neural networks ||  
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| 2016/07/08   ||Dong Wang  || Neural networks || Xing Chao; Aiting Liu; Andy Zhang; 白紫薇|| [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/    ||Dong Wang  || Deep learning (1)||  
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| 2016/07/11   ||Dong Wang  || Deep learning (1)|| Hang Luo; Jiyuan Zhang; Zhiyuan|| [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/   ||Dong Wang  || Deep learning (2)||  
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| 2016/07/12  ||Dong Wang  || Deep learning (2)|| Hang Luo; Jiyuan Zhang; Zhiyuan || [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/    ||Dong Wang  || Unsupervised learning ||  
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| 2016/07/13   || Caixia Wang  || Kernel methods || Caixia;Ziwei || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt6.%20kernel%20method.pptx 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/    ||Caixia Wang || Kernel methods ||  
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| 2016/07/18   ||Yang Feng || Graphical model (1) ||Jingyi Lin; Ying Shi; Yang Wang|| [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/    ||Dong Wang   || Probabilistic learning theory||
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| 2016/07/21   ||Dong Wang || Graphical model (2) ||Jingyi Lin; Ying Shi; Yang Wang||[http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt8.%20Graphical%20models-2.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson8-1_Graphical_model.m4v video(part 1)]  [http://arch.cslt.org/video/2016/sum-ML/lesson8-2_Graphical_model.m4v video(part 2)] [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/    ||Yang Feng || Graphical model: Bayesian approach ||  
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| 2016/07/25   ||Dong Wang || Unsupervised learning ||Lantian; Yixiang ||[http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt9.%20Unsupervised%20Learning.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson9-1_Unsupervised_learning.m4v video(part 1)]  [http://arch.cslt.org/video/2016/sum-ML/lesson9-2_Unsupervised_learning.m4v video(part 2)] [http://mlg.eng.cam.ac.uk/zoubin/papers/ul.pdf Unsupervised Learning from Zoubin Ghahramani, Cambridge] [http://www.mit.edu/~9.54/fall14/slides/Class13.pdf slides from MIT] [https://page.mi.fu-berlin.de/rojas/neural/chapter/K5.pdf Neural Networks - A Systematic Introduction, Raul Rojas] [http://www.cs.bu.edu/fac/gkollios/ada01/LectNotes/Clustering2.ppt slides from BU] [http://web.mit.edu/6.454/www/www_fall_2003/ihler/slides.pdf manifold slides from MIT]
 
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| 2016/07/    ||Yang Feng || Graphical model: Random field ||  
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| 2016/07/26   ||Dong Wang || Non parametric models ||Maoning Wang;  || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt10.%20Non-Parametric%20Learning.pptx slides]  [http://arch.cslt.org/video/2016/sum-ML/lesson10-1_Nonparameteric_model.m4v video(part 1)]  [http://arch.cslt.org/video/2016/sum-ML/lesson10-2_Nonparameteric_model.m4v video(part 2)] [http://cs229.stanford.edu/section/cs229-gaussian_processes.pdf Gaussian process]  [http://stat.columbia.edu/~porbanz/npb-tutorial.html Resource for non-parametric Bayesian] [http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/dp.pdf A good tutorial]
 
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| 2016/07/    ||Dong Wang  || No parametric models || [http://cs229.stanford.edu/section/cs229-gaussian_processes.pdf Gaussian process]
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| 2016/07/28   ||Maoning Wang  || Evolutionary learning ||Dong Wang;  ||[http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:EA_%E7%8E%8B%E5%8D%AF%E5%AE%81.pdf slides] [http://arch.cslt.org/video/2016/sum-ML/lesson11_Evolutional_algorithm.m4v video][http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:Introduction_to_Evolutionary_Computing.pdf Introduction_to_Evolutionary_Computing.pdf ‎]
 
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| 2016/07/    ||Dong Wang  || Reinforcement learning ||  
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| 2016/07/29   ||Dong Wang  || Reinforcement learning || Dong Wang || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt12.%20Reinforcement%20Learning.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson12-1_Reinforcement_learning.m4v video(part 1)]  [http://arch.cslt.org/video/2016/sum-ML/lesson12-2_Reinforcement_learning.m4v video(part 2)] [http://www.jair.org/media/301/live-301-1562-jair.pdf an old but good review] [http://rll.berkeley.edu/deeprlcourse/ state-of-the-art course]
 
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| 2016/07/    ||Maoning Wang  || Evolutionary learning ||  
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| 2016/08/02   ||Dong Wang  || Optimization || Caixia;  ||[http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt13.%20Optimization.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson13-1_Optimization.m4v video(part 1)]  [http://arch.cslt.org/video/2016/sum-ML/lesson13-2_Optimization.m4v video(part 2)]  [http://cs229.stanford.edu/section/cs229-cvxopt.pdf Convex optimization I] [http://cs229.stanford.edu/section/cs229-cvxopt2.pdf Convex optimization II]
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| 2016/07/   ||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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2017年1月6日 (五) 09:42的最后版本

  • Location: FIT-1-304


Date Speaker Title Owner Materials
2016/07/04 Dong Wang Machine learning overview Dong Wang slidesvideo(part 2)Algebra review probability review Gaussian distributionLearning theory
2016/07/05 Dong Wang Linear models Aiting Liu; Aodong Li slides video(part 1) video(part 2) NG's lecture 1 NG's lecture 2
2016/07/08 Dong Wang Neural networks Xing Chao; Aiting Liu; Andy Zhang; 白紫薇 slides video(part 1) video(part 2)
2016/07/11 Dong Wang Deep learning (1) Hang Luo; Jiyuan Zhang; Zhiyuan slides video(part 1) video(part 2) NIPS 2015 tutorial
2016/07/12 Dong Wang Deep learning (2) Hang Luo; Jiyuan Zhang; Zhiyuan slides video(part 1) video(part 2) Li Deng's ICASSP16 keynote
2016/07/13 Caixia Wang Kernel methods Caixia;Ziwei slides video Kernel method book pattern recognition 6-7
2016/07/18 Yang Feng Graphical model (1) Jingyi Lin; Ying Shi; Yang Wang slides video
2016/07/21 Dong Wang Graphical model (2) Jingyi Lin; Ying Shi; Yang Wang slides video(part 1) video(part 2) Yang's slides Jordan's lecture
2016/07/25 Dong Wang Unsupervised learning Lantian; Yixiang slides video(part 1) video(part 2) Unsupervised Learning from Zoubin Ghahramani, Cambridge slides from MIT Neural Networks - A Systematic Introduction, Raul Rojas slides from BU manifold slides from MIT
2016/07/26 Dong Wang Non parametric models Maoning Wang; slides video(part 1) video(part 2) Gaussian process Resource for non-parametric Bayesian A good tutorial
2016/07/28 Maoning Wang Evolutionary learning Dong Wang; slides videoIntroduction_to_Evolutionary_Computing.pdf ‎
2016/07/29 Dong Wang Reinforcement learning Dong Wang slides video(part 1) video(part 2) an old but good review state-of-the-art course
2016/08/02 Dong Wang Optimization Caixia; slides video(part 1) video(part 2) Convex optimization I Convex optimization II
取自“http://index.cslt.org/mediawiki/index.php?title=2016_Summer_Seminar_for_Machine_learning&oldid=25622”