SubjectsSubjects(version: 916)
Course, academic year 2022/2023
Data Mining - N500011
Title: Vytěžování znalostí z dat
Guaranteed by: CTU in Prague, Faculty of Information Technology (500)
Faculty: University of Chemistry and Technology, Prague
Actual: from 2021
Semester: summer
Points: summer s.:4
E-Credits: summer s.:4
Examination process: summer s.:
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
For type:  
Guarantor: Kordík Pavel doc.Ing. Ph.D.
Is interchangeable with: B500007
Examination dates   Schedule   
Annotation -
Last update: Jirát Jiří Ing. Ph.D. (10.01.2014)
Students are introduced to the basic methods of discovering knowledge in data. In particular, they learn the basic techniques of data preprocessing, multidimensional data visualization, statistical techniques of data transformation, and fundamental principles of knowledge discovery methods. Students will be aware of the relationships between model bias and variance, and know the fundamentals of assessing model quality. Data mining software is extensively used in the module. Students will be able to apply basic data mining tools to common problems (classification, regression, clustering).
Aim of the course -
Last update: Jirát Jiří Ing. Ph.D. (31.01.2014)

Students will be able to:

Understand knowledge discovery in data.

Literature -
Last update: Jirát Jiří Ing. Ph.D. (10.01.2014)

R:Larose, D. T. Discovering Knowledge in Data: An Introduction to Data Mining. Wiley-Interscience, 2004. ISBN 0471666572.

Learning resources -
Last update: Jirát Jiří Ing. Ph.D. (10.01.2014)

(login necessary)

Syllabus -
Last update: Jirát Jiří Ing. Ph.D. (10.01.2014)

1. Introduction to data mining, data preparation, data visualization.

2. Statistical analysis of data.

3. Data model, nearest neighbour classifier.

4. Training, validation and testing, model's quality evaluation.

5. Artificial neural networks in data mining.

6. Unsupervised neural networks - competitive learning

7. Probability and Bayesian classification.

8. Decision trees and rules.

9. Neural networks with supervised learning.

10. Cluster analysis.

11. Combining neural networks and models in general.

12. Data mining in the Clementine environment.

13. Text mining, Web mining, selected applications, new trends.

Registration requirements -
Last update: Jirát Jiří Ing. Ph.D. (31.01.2014)


Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Práce na individuálním projektu 0,5 14
Příprava na zkoušku a její absolvování 1,1 30
Účast na seminářích 1 28
4 / 4 100 / 112