SubjectsSubjects(version: 954)
Course, academic year 2019/2020
Data Mining Algorithms - M500002
Title: Algoritmy data miningu
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2019 to 2021
Semester: summer
Points: summer s.:4
E-Credits: summer s.:4
Examination process: summer s.:
Hours per week, examination: summer s.:2/1, C+Ex [HT]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Holeňa Martin doc. Ing. RNDr.
Interchangeability : N500012
Examination dates   Schedule   
Annotation -
In this course, we discuss most popular data mining algoritms and optimization techniques such as decision trees, support vector machines, multilayered perceptrons etc. We also explain theoreticaly basic elements of statistical learning that are essential for all data engineers.
Last update: Hladíková Jana (05.01.2018)
Aim of the course -

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

Last update: Hladíková Jana (05.01.2018)
Literature -

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

Last update: Svozil Daniel (04.11.2018)
Learning resources -

(login necessary)

Last update: Hladíková Jana (05.01.2018)
Syllabus -

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

Last update: Hladíková Jana (05.01.2018)
Registration requirements -

Statostocal data analysis, Data mining, Programming and algorithms

Last update: Svozil Daniel (08.02.2018)
Course completion requirements - Czech

Pro zı́skánı́ zápočtu je potřeba dostatek bodů ze semestrálnı́ práce. Zkouška se skládá z pı́semné přı́pravy a povinné ústnı́ části.

Last update: Svozil Daniel (07.02.2018)
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
Coursework assessment
Form Significance
Report from individual projects 40
Oral examination 60