1. | ![]() |
Introduction to the Machine Learning | 11. 9. 6 | ![]() |
2. | ![]() |
Bayesian Decision Theory | 11. 9. 8 | ![]() |
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Decision theory and Parametric probability models | 11. 9. 15 | ![]() |
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Parametric probability models | 11. 9. 19 | ![]() |
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Maximum Entropy + Model Selection and hidden variables | 11. 9. 27 | ![]() |
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Maximum Entropy + Model Selection and hidden variables cont | 11. 9. 29 | ![]() |
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Dimension reduction | 11. 10. 4 | ![]() |
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Singular Value Decomposition | 11. 10. 6 | ![]() |
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Fishers LDA | 11. 10. 11 | ![]() |
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K Means and EM | 11. 10. 12 | ![]() |
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More EM | 11. 10. 13 | ![]() |
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Decision Trees | 11. 10. 18 | ![]() |
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Linear Discrimination | 11. 10. 20 | ![]() |
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multilayer perception | 11. 10. 25 | ![]() |
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Support Vector machines | 11. 10. 27 | ![]() |
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AdaBoost | 11. 11. 1 | ![]() |
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AdaBoost (cont) | 11. 11. 3 | ![]() |
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MultiClass SVM | 11. 11. 8 | ![]() |
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MultiClass SVM cont | 11. 11. 9 | ![]() |
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Non linear Dimension Reduction | 11. 11. 23 | ![]() |
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Non linear Dimension Reduction cont | 11. 11. 24 | ![]() |
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Non linear Dimension Reduction cont | 11. 11. 29 | ![]() |
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Graphical Models | 11. 12. 6 | ![]() |
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summary of course | 11. 12. 8 | ![]() |