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Last update: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (09.05.2018)
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Last update: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (28.10.2019)
Students have to pass oral exam. |
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Last update: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (09.05.2018)
Zvára Karel: Regresní analýza, Academia 1989 Hebák, Hustopecký: Vícerozměrné statistické metody 1, 2, 3, Informatorium, 2007 Kolaczyk, Csardi: Statistical Analysis of Network Data with R, Springer, 2014 Munzert, Rubba, Meissner, Nyhuis: Automated Data Collection with R, Wiley, 2015 |
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Last update: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (28.10.2019)
Students have to pass oral exam. The requirements for the exam correspond to what has been done during lectures and practicals. |
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Last update: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (09.05.2018)
1) Basics of linear regression, logistic regression, lasso regression, principles of hypotheses testing, likelihood ratio tests, stepwise algorithms 2) Basics of multidimensional statistics - principle component analysis, factor analysis, cluster analysis 3) Discrimination measures - Kolmogorov-Smirnov, Gini coefficient, Somer’s d 4) Back test principles, cross validation and bootstrapping 5) Regression trees, random forests 6) Gradient boosting 7) Bayes networks, neural networks 8) Linear optimization, Support vector machine
Labs: Programming in R, practical work with data |