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)
|
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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)
|
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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)
|
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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)
|
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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)
|
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10. Approaximate Inference : 10.2
|
-
(PPT)
|
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10. Approaximate Inference : 10.3
|
ÀÌÁ¦±Ù
(PPT)
|
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10. Approaximate Inference : 10.4
|
±èº´Èñ
(PPT)
|
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10. Approaximate Inference : 10.5~10.6
|
À̽ÂÁØ
(PPT)
|
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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)
|
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12. Continuous Latent Variables : 12.3 ~ 12.4 |
Á¶µ¿¿¬
|
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07/03/07
|
13. Sequential Data : 13.1 ~ 13.2
|
±èÁø¿µ
(PPT)
|
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13. Sequential Data : 13.3
|
±¸º»¿õ
(PPT)
|
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07/10/07
|
14. Combining Models |
¾öÀçÈ«
(PPT)
|
|
Review Ch 8~14. Q & A. Ã¥ °Å ¸®
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