Basic data mining

Osnove podatkovnega rudarjenja

Course credits:

6.0 ECTS / 60 (30 hours of lectures, 0 hours of exercises, 30 hours of seminars, 0 hours other forms of work)

Course holder:

izr. prof. dr. Damjan Škulj






Second semester

Study degree

1. level

    Course execution:

  • Undergraduate Programme of Social Informatics

Short content

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. Techniques: Clustering; Neural networks; Association analysis; Classification and prediction: Decision trees; Discriminant analysis; Regression: linearr, general linear models, logistic, Poisson; Baysian methods: naive Bayesian classificator, Bayesian networks; Classification and predisition with neural networks; Support vector machines; Genetic algorithms; Boosting; 5. Models evaluation: Bias, variance and model complexity; Effective number of parameters; Vapnik-Chervonenkis dimension; Cross-validation

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Curriculum was last modified on: 11.05.2020