B_ZZD Knowledge Discovery in Database

University of Finance and Administration
Summer 2021
Extent and Intensity
2/0/0. 3 credit(s). Type of Completion: z (credit).
prof. Ing. Petr Berka, CSc. (seminar tutor)
Guaranteed by
prof. Ing. Petr Berka, CSc.
Department of Computer Science and Mathematics - Departments - University of Finance and Administration
Contact Person: Ivana Plačková
Timetable of Seminar Groups
B_ZZD/pAPH: Mon 10:30–11:14 S22, Mon 11:15–12:00 S22, P. Berka
B_ZZD/vAPH: Sat 20. 2. 9:45–11:15 S24, 11:30–13:00 S24, Fri 5. 3. 14:00–15:30 S24, P. Berka
B_ES Expert Systems
The requirement for the completion of this course is completion of the course B_ES.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
Students will get familiar with the methods used for knowledge discovery in databases. The lectures focus on different machine learning algorithms. The students will also work wit one of the widely used data mining systems.
Learning outcomes
At the end of the course students should be able to:
- understand the role of KDD for data analysis,
- understand the basic principles of various data mining algorithms,
- understand the basic methods of evaluation of created models,
- understand basic preprocessing operations,
- formulate KDD tasks for real-world data
  • 1. The process of KDD: tasks, steps, methodologies
  • 2. The background for KDD: databases, statistics,
  • 3. Machine learning: basic tasks
  • 4. decision trees,
  • 5. association rules,
  • 6. decision rules,
  • 7. neural nets, gentic algorithms,
  • 8. bayesian methods, instance based learning
  • 9. Evaluation what has been learned
  • 10. Data preprocessing methods
  • 11. Data mining using Weka
  • 12. Test
    required literature
  • Berka,P.Dobývání znalostí z databází. Academia, Praha 2003. ISBN 80-200-1062-9.
    recommended literature
  • Kelemen, J. a kol.: Pozvanie do znalostnej spoločnosti. Iura Edition, Bratislava, 2007
  • Witten I., Frank E.: Data Mining, Practical Machine Learning Tools and Techniques with Java. Fourth edition. Morgan Kaufmann, San Francisco 2016
    not specified
  • Han J., Kerber M.: Data Mining, Concepts and Techniques. Morgan Kaufmann, San Francisco 2001
Teaching methods
- lectures and seminars in full-time study,
- tutorials in part-time study,
- case study of data analysis using data mining methods
Assessment methods
- written final test consisting of unstructured questions (min 60% points)
- case study of data analysis using data mining methods
Language of instruction
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Information on the extent and intensity of the course: 6 hodin KS/semestr.
The course is also listed under the following terms Winter 2007, Summer 2009, Summer 2010, Summer 2011, Winter 2011, summer 2012, Summer 2013, Summer 2014, Summer 2015, Summer 2016, Summer 2017, Summer 2018, Summer 2019, Summer 2020.
  • Enrolment Statistics (recent)
  • Permalink: https://is.vsfs.cz/course/vsfs/summer2021/B_ZZD