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 ¾öÀçÈ«
(PPT)

     1. Introduction : 1.4 ~ 1.6

ÇÏÁ¤¿ì
(PPT)
04/03/07      2. Probability Distribution : 2.1 ~ 2.3.5 ³ë¿µ±Õ
(PPT)

     2. Probability Distribution : 2.3.6 ~ 2.5

±èº´Èñ
(PPT)

04/10/07

     3. Linear Models for Regression : 3.1 ~ 3.3

ÀÌÁ¦±Ù
(PPT)

     3. Linear Models for Regression : 3.4 ~ 3.6

ÀÌÁ¦±Ù
(PPT)

04/17/07

     4. Linear Models for Classification : 4.1 ~ 4.2

ÀÓÈñ¿õ
(PPT)

     4. Linear Models for Classification : 4.3 ~ 4.5

À̽ÂÁØ
(PPT)

04/24/07

     5. Neural Networks : 5.1 ~ 5.4

¼®È£½Ä
(PPT)

     5. Neural Networks : 5.5 ~ 5.7

ÀåÇÏ¿µ
(PPT)

05/01/07

     6. Kernel Methods 

±èÁؽÄ
(PPT)

05/08/07

Review Ch 1~7. Q & A

 

     7. Sparse Kernel Machines 

±è   ¼±
(PPT)

05/15/07

     8. Graphical Models : 8.1 ~ 8.2

Çã¹Î¿À
(PPT)

     8. Graphical Models : 8.3 ~ 8.4

±èº´Èñ
(PPT)

06/05/07

     9. Mixture Models and EM

¾çÁø»ê
(PPT)

06/12/07

   10. Approaximate Inference : 10.1

¾çÁø»ê
(PPT)

   10. Approaximate Inference : 10.2

-
(PPT)

   10. Approaximate Inference : 10.3

ÀÌÁ¦±Ù
(PPT)

   10. Approaximate Inference : 10.4

±èº´Èñ
(PPT)

   10. Approaximate Inference : 10.5~10.6

À̽ÂÁØ
(PPT)

   10. Approaximate Inference : 10.7

±èº´Èñ
(PPT)

06/19/07

   11. Sampling Methods

ÀÌÀÎÈñ
(PPT)

06/28/07

   12. Continuous Latent Variables : 12.1 ~ 12.2   

Á¶µ¿¿¬
(PPT)

   12. Continuous Latent Variables : 12.3 ~ 12.4

Á¶µ¿¿¬

07/03/07

   13. Sequential Data : 13.1 ~ 13.2

±èÁø¿µ
(PPT)

   13. Sequential Data : 13.3

±¸º»¿õ
(PPT)

07/10/07

   14. Combining Models

¾öÀçÈ«
(PPT)

Review Ch 8~14. Q & A. Ã¥ °Å ¸®

 

This page is maintained by
Last update: July 03, 2007.