Basic data mining
Osnove podatkovnega rudarjenja
6.00 ECTS / 60 (30 hours of lectures, 0 hours of exercises, 30 hours of seminars, 0 hours other forms of work)
izr. prof. dr. Damjan Škulj
- Asst. Prof. Dr. Luka Kronegger
- Undergraduate Programme of Social Informatics
Introduction: steps of data mining analysis, data preparation and preliminary analysis, overview of methods, CRISP-DM methodology.
data presentation and manipulation: standard forms of data presentation and manipulation of tabular data, databases, and data warehouses, presentation of web and textual data.
Software: types, basic characteristics, comparison, free packages.
Classification and prediction:
Regression: linearr, general linear models, logistic, Poisson;
Baysian methods: naive Bayesian classificator, Bayesian networks;
Classification and predisition with neural networks;
Support vector machines;
5. Models evaluation:
Bias, variance and model complexity;
Effective number of parameters;
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Curriculum was last modified on: 11.05.2020