[2006/2007: S2] Machine Learning Seminar

Textbook: Christopher M. Bishop, Pattern Recognition and Machine Learning,                   Springer,  2006.
Room: 301-421
Time: TUE/THU 10:00 ~ 12:00

*Notice: Seminar on the Ch10 is postponed to the next round. Currently, no file.

Date
(mm/dd/yy)
Chapter - Section Presenter
12/26/06(TUE)      1. Introduction : 1.1 ~ 1.3
(PPT)

     1. Introduction : 1.4 ~ 1.6


(PPT)
12/28/06(THU)      2. Probability Distribution : 2.1 ~ 2.3.5 ְ
(PPT)

     2. Probability Distribution : 2.3.6 ~ 2.5


(PPT)

01/02/07(TUE)

     3. Linear Models for Regression : 3.1 ~ 3.3


(PPT)

     3. Linear Models for Regression : 3.4 ~ 3.6


(PPT)

01/04/07(THU)

     4. Linear Models for Classification : 4.1 ~ 4.2


(PPT)

     4. Linear Models for Classification : 4.3 ~ 4.5

̽
(PPT)

01/09/07(TUE)

     5. Neural Networks : 5.1 ~ 5.4

ο
(PPT)

     5. Neural Networks : 5.5 ~ 5.7

Ͽ
(PPT)

01/11/07(THU)

     6. Kernel Methods 

ؽ
(PPT)

 

 

01/16/07(TUE)

Review Ch 1~7. Q & A

 

     7. Sparse Kernel Machines 

  
(PPT)

01/18/07(THU)

     8. Graphical Models : 8.1 ~ 8.2


(PPT)

     8. Graphical Models : 8.3 ~ 8.4


(PPT)

01/23/07(TUE)

     9. Mixture Models and EM

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(PPT)

   10. Approaximate Inference : 10.1 ~ 10.3


(PPT)

01/25/07(THU)

   10. Approaximate Inference : 10.4 ~ 10.7


(PPT)

   11. Sampling Methods


(PPT)

01/30/07(TUE)

   12. Continuous Latent Variables : 12.1 ~ 12.2   


(PPT)

   12. Continuous Latent Variables : 12.3 ~ 12.4


(PPT)

02/06/07(TUE)

   13. Sequential Data : 13.1 ~ 13.2


(PPT)

   13. Sequential Data : 13.3


(PPT)

02/08/07(THU)

   14. Combining Models

ȫ
(PPT)

Review Ch 8~14. Q & A. å

 

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Last update: February 26, 2007.