Textbook: Tom Mitchell, Machine Learning, McGraw Hill, 1998
Room: 301-412
Time: 14:00 ~ 17:00
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Date |
Chapter |
Speaker |
Supervisor |
01/22/08 |
1. Introduction |
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2. Concept Learning |
¹ÚÂùÈÆ | ||
01/24/08 |
3. Decision Tree Learning |
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ÀåÇÏ¿µ |
4. Artificial Neural Networks |
½Å¿øÁø | ||
01/29/08 |
5. Evaluating Hypotheses |
°í¿µ±æ |
±èº´Èñ |
6. Bayesian Learning |
ÀÌ»óÀ± | ||
01/31/08 |
7. Computational Learning Theory |
±è¹ÎÇõ |
¼®È£½Ä |
8. Instance-Based Learning |
±èÈÆÈñ | ||
02/12/08 |
9. Genetic Algorithms |
ÀÌÁöÈÆ |
ÀÌÀÎÈñ |
10. Learning Set of Rules |
±èÁÖ°æ | ||
02/14/08 |
11. Analytical Learning |
ÇÏÁ¤¿ì |
¾öÀçÈ« |
12. Combining Inductive and Analytical Learning |
À̼º¹è | ||
02/19/08 |
13. Reinforcement Learning |
±è±ÇÀÏ |
À̽ÂÁØ |
14. Generative and Discriminative Classifiers:
|
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02/21/08 |
ÀÌÁ¦±Ù | ||
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Introduction to Cognitive science and ML |
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02/28/08 |
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Âü°í : PPT ÀÚ·á´Â 2007 ³â 2 Çб⠱â°èÇнÀ ppt°¡ ±¦Âú¾Æ¼ À̰ÍÀ»(2007ML_Fall) »ç¿ëÇϼŵµ ÁÁ°í,
http://bi.snu.ac.kr/SEMINAR/ML/¿¡ ³õ¿©ÀÖ´Â ±âÁ¸ ML ¼¼¹Ì³ª ÀڷḦ ½áµµ ÁÁ°Ú½À´Ï´Ù.
14 Àå ¿øº»ÀÇ ¸µÅ©´Â http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf ÀÔ´Ï´Ù.