[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 ±è±ÇÀÏ
(PPT)

     1. Introduction : 1.4 ~ 1.6

ÇÏÁ¤¿ì
(PPT)
12/28/06(THU)      2. Probability Distribution : 2.1 ~ 2.3.5 ±èÁÖ°æ
(PPT)

     2. Probability Distribution : 2.3.6 ~ 2.5

±è¹ÎÇõ
(PPT)

01/02/07(TUE)

     3. Linear Models for Regression : 3.1 ~ 3.3

³ë¿µ±Õ
(PPT)

     3. Linear Models for Regression : 3.4 ~ 3.6

³ë¿µ±Õ
(PPT)

01/04/07(THU)

     4. Linear Models for Classification : 4.1 ~ 4.2

ÀÓÈñ¿õ
(PPT)

     4. Linear Models for Classification : 4.3 ~ 4.5

À̽ÂÁØ
(PPT)

01/09/07(TUE)

     5. Neural Networks : 5.1 ~ 5.4

Çã¹Î¿À
(PPT)

     5. Neural Networks : 5.5 ~ 5.7

ÀåÇÏ¿µ
(PPT)

01/11/07(THU)

     6. Kernel Methods 

±èÁؽÄ
(PPT)

 

 

01/16/07(TUE)

Review Ch 1~7. Q & A

 

     7. Sparse Kernel Machines 

±è   ¼±
(PPT)

01/18/07(THU)

     8. Graphical Models : 8.1 ~ 8.2

³²Áø¿ì
(PPT)

     8. Graphical Models : 8.3 ~ 8.4

±èº´Èñ
(PPT)

01/23/07(TUE)

     9. Mixture Models and EM

¼®È£½Ä
(PPT)

   10. Approaximate Inference : 10.1 ~ 10.3

±èº´Èñ
(PPT)

01/25/07(THU)

   10. Approaximate Inference : 10.4 ~ 10.7

³ë¿µ±Õ
(PPT)

   11. Sampling Methods

ÀÌÀÎÈñ
(PPT)

01/30/07(TUE)

   12. Continuous Latent Variables : 12.1 ~ 12.2   

±è¼öÁø
(PPT)

   12. Continuous Latent Variables : 12.3 ~ 12.4

ÀÌÁ¦±Ù
(PPT)

02/06/07(TUE)

   13. Sequential Data : 13.1 ~ 13.2

±èÁø¿µ
(PPT)

   13. Sequential Data : 13.3

±¸º»¿õ
(PPT)

02/08/07(THU)

   14. Combining Models

¾öÀçÈ«
(PPT)

Review Ch 8~14. Q & A. Ã¥ °Å ¸®

 

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Last update: February 26, 2007.