2007 Machine Learning Seminar

Textbook: Christopher M. Bishop, Pattern Recognition and Machine Learning,                   Springer,  2006.
Room: 301-421
Time: TUE 16:00 ~ 18:00

Date
(mm/dd/yy)
Chapter - Section Presenter
03/27/07      1. Introduction : 1.1 ~ 1.3 ȫ
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     1. Introduction : 1.4 ~ 1.6


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04/03/07      2. Probability Distribution : 2.1 ~ 2.3.5
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     2. Probability Distribution : 2.3.6 ~ 2.5


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04/10/07

     3. Linear Models for Regression : 3.1 ~ 3.3


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     3. Linear Models for Regression : 3.4 ~ 3.6


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04/17/07

     4. Linear Models for Classification : 4.1 ~ 4.2


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     4. Linear Models for Classification : 4.3 ~ 4.5

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04/24/07

     5. Neural Networks : 5.1 ~ 5.4

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     5. Neural Networks : 5.5 ~ 5.7

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05/01/07

     6. Kernel Methods 

ؽ
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05/08/07

Review Ch 1~7. Q & A

 

     7. Sparse Kernel Machines 

  
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05/15/07

     8. Graphical Models : 8.1 ~ 8.2

ο
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     8. Graphical Models : 8.3 ~ 8.4


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06/05/07

     9. Mixture Models and EM


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06/12/07

   10. Approaximate Inference : 10.1


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   10. Approaximate Inference : 10.2

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   10. Approaximate Inference : 10.3


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   10. Approaximate Inference : 10.4


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   10. Approaximate Inference : 10.5~10.6

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   10. Approaximate Inference : 10.7


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06/19/07

   11. Sampling Methods


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06/28/07

   12. Continuous Latent Variables : 12.1 ~ 12.2   


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   12. Continuous Latent Variables : 12.3 ~ 12.4


07/03/07

   13. Sequential Data : 13.1 ~ 13.2


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   13. Sequential Data : 13.3


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07/10/07

   14. Combining Models

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Review Ch 8~14. Q & A. å

 

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Last update: July 03, 2007.