B_DZD Knowledge Discovery in Databases

University of Finance and Administration
Winter 2025
Extent and Intensity
2/0/0. 3 credit(s). Type of Completion: z (credit).
Teacher(s)
Ing. Renata Janošcová, Ph.D. (seminar tutor)
Guaranteed by
Ing. Renata Janošcová, Ph.D.
Department of Computer Science and Mathematics – Departments – University of Finance and Administration
Contact Person: Ivana Plačková
Timetable of Seminar Groups
B_DZD/pAPH: each odd Thursday 12:15–12:59 E230, each odd Thursday 13:00–13:45 E230, each odd Thursday 14:00–14:44 E230, each odd Thursday 14:45–15:30 E230, except Thu 4. 12. ; and Thu 4. 12. 12:15–13:45 E004, 14:00–15:30 E004, R. Janošcová
B_DZD/vAPH: Fri 26. 9. 14:00–15:30 E303PC, 15:45–17:15 E303PC, Fri 31. 10. 14:00–15:30 E303PC, 15:45–17:15 E303PC, Fri 28. 11. 14:00–15:30 E303PC, 15:45–17:15 E303PC, R. Janošcová
Prerequisites
Tento předmět nemá žádné předpoklady.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The aim of the course is to introduce students to methods of automated knowledge acquisition from databases. The presentation will focus on individual algorithms from the field of machine learning. Students will get to know one of the used KDD (Knowledge Discovery in Databases) systems, usually Weka.
Learning outcomes
At the end of the course students should be able to:
- understand the role of KDD for data analysis;
- understand different data mining tasks;
- understand the basic principles of various data mining algorithms;
- understand the basic methods of evaluation of created models;
- understand data preprocessing problems.
Syllabus
  • 1. Data, information, knowledge, databases.
  • 2. Knowledge extraction process.
  • 3. Machine learning; WEKA...
  • 4. Symbolic methods of classification and prediction.
  • 5. Subsymbolic methods of classification and prediction.
  • 6. Dependency analysis.
  • 7. Segmentation.
  • 8. Evaluation of the found knowledge.
  • 9. New trends; Assignment of credit work.
  • 10. Solution of credit work.
  • 11. Presentation of credit work.
  • 12. Presentation of credit work.
Literature
    required literature
  • BORKOVCOVÁ, Monika, Jan MERTA et all. Enviromentální analýza dat. Žilina: EDIS, 2025. ISBN: 978-80-554-2186-5. Dostupná po registraci z: https://edis.uniza.sk/produkt/7421/Environmentalni-analyza-dat/.
  • BERKA, P. a J. GÓRECKI. [pdf] Dolování dat. Skripta SU OPF, Karviná, 2017. Dostupná z: https://is.slu.cz/el/opf/zima2021/INMNPDOD/um/Dolovani_dat.pdf
  • Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J.Pal.2017.Data Mining:Practical Machine Learning Tools and Techniques. Vol.Fourth edition. Cambridge,MA, US: Morgan Kaufmann. ISBN 9780128042915.EBSCO (e-kniha přes IS VŠFS)
    recommended literature
  • BERKA,P. Dobývání znalostí z databází. Academia, Praha 2003. ISBN 80-200-1062-9
  • POTANČOK, Martin, Jan POUR a Veronika CHRAMOSTOVÁ. Business analytika v praxi. Praha: Oeconomica, nakladatelství VŠE, 2020. ISBN 978-80-245-2382-8.
  • WITTEN, Ian H., Frank, EIBE et all. Data Mining : Practical Machine Learning Tools and Techniques. Edition 5. Cambridge,MA, US: Morgan Kaufmann, 2025. ISBN 9780443158889.
  • JAMSA, K. Introduction to Data Mining and Analytics. Burlington : Jones & Bartlett Learning, 2021. ISBN: 9781284180909. EBSCO (e-kniha, dostupná přes IS VŠFS)
  • NAQVI, Al. Artificial Intelligence for Audit, Forensic Accounting, and Valuation : A Strategic Perspective, John Wiley & Sons, Incorporated, 2020. ProQuest Ebook Central.ISBN:9781119601883.ProQuest Ebook (e-kniha přes IS VŠFS)
Teaching methods
Teaching takes place in the form of exercises and lectures in a full-time form of study; concentration in the combined form of study;
Minimum compulsory participation in classes is not specified, but successful completion of the subject requires it to a sufficient extent.
Assessment methods
The course is concluded with a credit in the form of a team credit work.
CREDIT is awarded in both forms (PS and KS) for teamwork (3-6 students), which involves creating a professional research according to the teacher's assignment, with the support of AI. The assignment can be found in the study materials and in MS Teams. The credit work is required to be submitted in the submission center with a similarity of less or equal 20%.

ISP students - the same conditions as full-time students.
ISP and REPEATING students - agree with the teacher as soon as possible on the method of assessment.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Teacher's information
https://is.vsfs.cz/go/b3r857
The course website (above) is a LINK to the team (subject) in MS Teams (then select your teacher's channel, or the form of study - KS or PS).
It is necessary to install MS Teams in the form of an application on your laptop.
CONTACTS for the teacher: guarantor Ing. Renata Janošcová, Ph.D 37037@mail.vsfs.cz.
CONSULTATION: information can be found on the teacher's personal pages in IS VŠFS (Teaching). https://is.vsfs.cz/auth/osoba/37037#vyuka
ISP and REPEATING students: At the very beginning of the semester (first - second week), contact your teacher and agree on specific conditions of attendance and evaluation.
I RECOMMEND submitting a request for inclusion in a timetable (seminar) group for a specific teacher according to instructions from the study department. 3 credits: 75 - 90 hours of study load.
The course is also listed under the following terms Winter 2020, Winter 2021, Winter 2022, Winter 2023, Winter 2024.
  • Enrolment Statistics (recent)
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