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

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| 2016/07/11    ||Dong Wang  || Deep learning (1)|| [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt4.%20Deep%20learning-1.pptx slides]  [http://www.iro.umontreal.ca/~bengioy/talks/DL-Tutorial-NIPS2015.pdf NIPS 2015 tutorial]
 
| 2016/07/11    ||Dong Wang  || Deep learning (1)|| [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt4.%20Deep%20learning-1.pptx slides]  [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/    ||Dong Wang  || Deep learning (2)|| [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt5.%20Deep%20learning-2.pptx slides] [http://www.icassp2016.org/SP16_PlenaryDeng_Slides.pdf Li Deng's ICASSP16 keynote]
 
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| 2016/07/    ||Dong Wang  || Unsupervised learning ||  
 
| 2016/07/    ||Dong Wang  || Unsupervised learning ||  

2016年7月12日 (二) 09:37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) slides Li Deng's ICASSP16 keynote
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