Subjects

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Data Mining - B500007

Title:	Vytěžování znalostí z dat
Guaranteed by:	Department of Informatics and Chemistry (143)
Faculty:	Faculty of Chemical Technology
Actual:	from 2021
Semester:	both
Points:	4
E-Credits:	4
Examination process:
Hours per week, examination:	2/2, C+Ex [HT]
Capacity:	winter:unlimited / unlimited (unknown) summer:unknown / unknown (unknown)
Min. number of students:	unlimited
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time
Level:
Note:	you can enroll for the course in winter and in summer semester

Guarantor:	Kordík Pavel doc. Ing. Ph.D.
Interchangeability :	N500011
Is interchangeable with:	B500010, B500011

Examination dates

Annotation -

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).

Last update: Kubová Petra (02.01.2018)

Aim of the course -

Students will be able to:

Understand knowledge discovery in data.

Last update: Kubová Petra (02.01.2018)

Course completion requirements - Czech

Pro zı́skánı́ zápočtu je potřeba dostatek bodů ze programovacích úloh a testu. Zkouška se skládá z povinné pı́semné části.

Last update: Svozil Daniel (07.02.2018)

Literature -

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

R: Berka, P. Dobývání znalostí z databází. Praha: Academia, 2003

A: L. Pierson: Data Science for Dummies (2nd edition), 2017

Last update: Svozil Daniel (26.03.2019)

Syllabus -

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.

Last update: Kubová Petra (02.01.2018)

Learning resources -

https://edux.fit.cvut.cz/courses/BI-VZD/

(login necessary)

Last update: Kubová Petra (02.01.2018)

Registration requirements -

none

Last update: Kubová Petra (02.01.2018)

Teaching methods
	Activity	Credits	Hours
	Účast na přednáškách	1	28
	Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi	1	28
	Příprava na zkoušku a její absolvování	1	28
	Účast na seminářích	1	28
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