SubjectsSubjects(version: 949)
Course, academic year 2023/2024
Data Mining Algorithms - M500002
Title: Algoritmy data miningu
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2022
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
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
For type:  
Guarantor: Holeňa Martin doc. Ing. RNDr.
Interchangeability : N500012
Annotation -
Last update: Hladíková Jana (05.01.2018)
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.
Aim of the course -
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

Literature -
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

Learning resources -
Last update: Hladíková Jana (05.01.2018)

(login necessary)

Syllabus -
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

Registration requirements -
Last update: Svozil Daniel prof. Mgr. Ph.D. (08.02.2018)

Statostocal data analysis, Data mining, Programming and algorithms

Course completion requirements - Czech
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