B_DZD Knowledge Discovery in Databases

University of Finance and Administration
Winter 2022
Extent and Intensity
0/2/0. 3 credit(s). Type of Completion: z (credit).
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/cEKPH: Thu 15:45–16:29 E307, Thu 16:30–17:15 E307, except Thu 27. 10., except Thu 3. 11., except Thu 15. 12. ; and Thu 27. 10. 15:45–17:15 E228, Thu 3. 11. 15:45–17:15 E228, Thu 15. 12. 8:45–10:15 S14, R. Janošcová
B_DZD/vEKPH: Fri 30. 9. 17:30–19:00 E228, 19:15–20:45 E228, Fri 14. 10. 17:30–19:00 E227, 19:15–20:45 E227, Fri 4. 11. 17:30–19:00 E228, 19:15–20:45 E228, R. Janošcová
Tento předmět nemá žádné předpoklady.
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 data mining tasks and suitable 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 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;
- formulate KDD tasks for real-world data
  • 1. Data, information, knowledge
  • 2. Process of knowledge discovery in databases
  • 3 Machine learning; Weka...
  • 4. Symbolic classification and prediction
  • 5. Subsymbolic classification and prediction
  • 6. Dependency analysis
  • 7. Segmentation
  • 8. Evaluation of created models
  • 9. New trends; Case study assignment.
  • 10.Case study solutions
  • 11.Case study solutions
  • 12.Presentations of case studies
    required literature
  • POTANČOK, Martin, Jan POUR a Veronika CHRAMOSTOVÁ. Business analytika v praxi. Praha: Oeconomica, nakladatelství VŠE, 2020. ISBN 978-80-245-2382-8.
    recommended literature
  • 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)
    not specified
  • BERKA, P. 4IZ450 – Dobývání znalostí z databází. Praha: VŠE, 2006 - 2021. https://sorry.vse.cz/~berka/4IZ450/
  • BERKA,P. Dobývání znalostí z databází. Academia, Praha 2003. ISBN 80-200-1062-9
  • 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)
  • KELEMEN, J. a kol.: Pozvanie do znalostnej spoločnosti. Iura Edition, Bratislava, 2007. ISBN 9788080781491.
Teaching methods
- Exercises, lectures, reading in the face-to-face form of study;
- Teaching in the combined study will take place in the form of concentration, where, in addition to a condensed presentation of the topic, previous self-study is also expected.
- In both forms - case studies of data analysis using methods of knowledge acquisition.
Assessment methods
The subject is completed with a CREDIT (3 credits).
CREDIT - Full-time study: Granting of credit is tied to:
- fulfillment of the obligation of active attendance min. 75% (i.e. active participation in the course of teaching).
- Elaboration and presentation of a team case study - assignment see IS (Zadani_PS_2022.docx).
CREDIT - Combined study: Granting of credit is tied to:
- fulfillment of the obligation of active attendance in  min. in the range of 50% at training sessions (active participation in the course of teaching).
- Development and presentation of a case study - assignment see IS (Zadani_PS_2022.docx).
ATTENTION: For all forms it is necessary:
- handing in credit work according to the assignment in the drop-in office (one per team).

Late attendance will not be accepted for students during classes, and if a class, tutorial or consultation is taking place via Microsoft Teams, it is expected to turn on the camera and microphone when prompted by the teacher.
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: 12 hodin KS/semestr.
Teacher's information
Contact the teacher: 37037@mail.vsfs.cz

CONSULTATION: in person according to the IS schedule on the teacher's personal page: https://is.vsfs.cz/auth/osoba/37037#
or online in MS Teams according to the listed dates: https://is.vsfs.cz/go/fc4rd8

ISP students: Contact the teacher right at the beginning of AR and agree on specific conditions.

ISP students have to prepare a case study of data analysis according to the assignment for full-time studies with an increase in the limit of the number of methods used to 3. They can work individually.

3 credits: 75 - 90 hours of study load.

The course is also listed under the following terms Winter 2020, Winter 2021.
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