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Last update: Jirát Jiří Ing. Ph.D. (31.01.2014)
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Last update: Jirát Jiří Ing. Ph.D. (31.01.2014)
Students will be able to: use theoretical background that is needed for skillful application of data mining algoritms in the field of classification, regression and clustering |
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Last update: Jirát Jiří Ing. Ph.D. (31.01.2014)
R:Hastie T.,Tibshirani R.,Friedman J., The Elements of Statistical Learning, Data Mining, Inference and Prediction, Springer, 2011 |
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Last update: Jirát Jiří Ing. Ph.D. (31.01.2014)
https://edux.fit.cvut.cz/courses/MI-ADM (login necessary) |
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Last update: Jirát Jiří Ing. Ph.D. (31.01.2014)
1. Introduction to data mining, classification, prediction, K-NN algorithm and variants 2. Model, evaluation, plasticity regularization 3. Classification and Regression from statistical point of view 4. Decision Trees (C4.5, CART, MARS algorithms) 5. Classification by means of perceptrons and its generalization 6. Linear, polynomial and logistic regression, LMS, MLE algorithms 7. Nonlinear SVM-classifiers and the SV-regression 8. Inductive modelling - GMDH MIA, COMBI 9. Nonlinear regression by multilayered perceptrons 10. Ensemble models (Adaboost algorithm) 11. Statistical approach to neural networks 12. Cluster analysis (K-means, agglomerative clustering, neural gas, SOM) 13. A statistical approach to number of hidden neurons selection |
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Last update: Jirát Jiří Ing. Ph.D. (31.01.2014)
Statistics |
Teaching methods | ||||
Activity | Credits | Hours | ||
Účast na přednáškách | 1 | 28 | ||
Práce na individuálním projektu | 2.2 | 61 | ||
Účast na seminářích | 0.5 | 14 | ||
4 / 4 | 103 / 112 |