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

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| 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]
 
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| 2016/07/    ||Dong Wang  || Neural networks ||  
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| 2016/07/    ||Dong Wang  || Neural networks || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt2.%20nNeural%20Network.pptx slides] []
 
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| 2016/07/    ||Dong Wang  || Deep learning (1)||  
 
| 2016/07/    ||Dong Wang  || Deep learning (1)||  

2016年7月8日 (五) 09:33的版本

  • Location: FIT-1-304


Date Speaker Title Materials
2016/07/04 Dong Wang Machine learning overview slidesAlgebra review probability review Gaussian distributionLearning theory
2016/07/05 Dong Wang Linear models slides NG's lecture 1 NG's lecture 2
2016/07/ Dong Wang Neural networks slides []
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 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