| 1 |
Introduction to Data Mining |
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| 2 |
Introduction to Data Mining |
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| 3 |
Data, pre-processing and post-processing |
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| 4 |
Similarity and Distance. Metrics |
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| 5 |
Similarity and Distance. Metrics |
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| 6 |
Clustering, K-means algorithm |
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| 7 |
Implementation of K-means algorithm |
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| 8 |
Singular Value Decomposition (SVD) |
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| 9 |
Implementation and application of SVD in image processing |
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| 10 |
Classification. Decision Tree Algorithm |
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| 11 |
Implementation of decision tree algorithm |
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| 12 |
Classification. k-Nearest Neighbor, Logistic Regression |
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| 13 |
Implementation of Logistic Regression |
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| 14 |
Implementation of k-Nearest Neighbor |
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| 15 |
Classification, SVM algorithm |
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| 16 |
Implementation of SVM algorithm |
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| 17 |
Classification. Naive Bayes Algorithm |
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| 18 |
Implementation of Naive Bayes algorithm |
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| 19 |
Neural Networks |
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| 20 |
Data mining in medicine |
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| 21 |
Data mining in medicine |
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| 22 |
Implementation with a real world medicine data |
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| 23 |
Implementation with a real world medicine data |
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| 24 |
Implementation with a real world medicine data |
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