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Last update: Hladíková Jana (05.01.2018)
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Last update: Hladíková Jana (05.01.2018)
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: Svozil Daniel prof. Mgr. Ph.D. (04.11.2018)
R:Berka P. Dobývání znalostí z databází. Praha: Academia, 2003. ISBN 80-200-1062-9 R:Hastie T.,Tibshirani R.,Friedman J., The Elements of Statistical Learning, Data Mining, Inference and Prediction, Springer, 2016, ISBN: 978-0387848570 |
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Last update: Hladíková Jana (05.01.2018)
https://edux.fit.cvut.cz/courses/MI-ADM (login necessary) |
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Last update: Hladíková Jana (05.01.2018)
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: Svozil Daniel prof. Mgr. Ph.D. (08.02.2018)
Statostocal data analysis, Data mining, Programming and algorithms |
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Last update: Svozil Daniel prof. Mgr. Ph.D. (07.02.2018)
Pro zı́skánı́ zápočtu je potřeba dostatek bodů ze semestrálnı́ práce. Zkouška se skládá z pı́semné přı́pravy a povinné ústnı́ části. |
Teaching methods | ||||
Activity | Credits | Hours | ||
Účast na přednáškách | 1 | 28 | ||
Práce na individuálním projektu | 2 | 56 | ||
Účast na seminářích | 0.5 | 14 | ||
4 / 4 | 98 / 112 |