VSFS:B_DZD Knowledge Disc. in Databases - Course Information

B_DZD Knowledge Discovery in Databases

Extent and Intensity

0/2/0. 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/cEKKV: each odd Thursday 14:00–14:44 KV305PC, each odd Thursday 14:45–15:30 KV305PC, each odd Thursday 15:45–16:29 KV305PC, each odd Thursday 16:30–17:15 KV305PC, except Thu 10. 10. ; and Thu 28. 11. 10:30–12:00 KV305PC, 12:15–13:45 KV305PC, R. Janošcová
B_DZD/cEKPH: each odd Wednesday 15:45–16:29 E228, each odd Wednesday 16:30–17:15 E228, each odd Wednesday 17:30–18:14 E228, each odd Wednesday 18:15–19:00 E228, R. Janošcová
B_DZD/vEKPH: Sat 26. 10. 9:45–11:15 E227, 11:30–13:00 E227, Sat 9. 11. 9:45–11:15 E227, 11:30–13:00 E227, Sat 23. 11. 9:45–11:15 E227, 11:30–13:00 E227, 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. In the practical part, students will work with the selected KDD system (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;
- formulate KDD tasks for real-world data

Syllabus

• 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

Literature

required literature
• POTANČOK, Martin, Jan POUR a Veronika CHRAMOSTOVÁ. Business analytika v praxi. Praha: Oeconomica, nakladatelství VŠE, 2020. ISBN 978-80-245-2382-8.
• 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

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)
• 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 (data analysis in the selected KDD system), or elaboration of a theoretical KDD topic according to the teacher's instructions (details will be published in MS Teams).
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).
- The same as for full-time students - Elaboration of a team case study or elaboration of a theoretical topic of KDD.
ATTENTION: For all forms it is necessary:
- handing in credit work according to the assignment in the drop-in office with a similarity less than or equal to 20 %.

If a lesson, tutorial or consultation will take place via Microsoft Teams, it is expected to turn on the camera and microphone when prompted by the teacher.

Language of instruction

Czech

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

https://is.vsfs.cz/go/go690a
The subject website presents a LINK to the team (subject) in MS Teams (then select your teacher's channel).

Study materials (lectures, video recordings, ...) of the subject can be found in IS VŠFS: https://is.vsfs.cz/auth/el/vsfs/zima2024/B_DZD/

CONTACTS for the teacher: guarantor Ing. Renata Janošcová, Ph.D - 37037@mail.vsfs.cz.

CONSULTATION: information can be found on the personal pages of teachers in IS VŠFS (Teaching).

ISP and REPEATING students: Contact your teacher at the beginning of the semester (first - second week) and agree on the specific conditions of attendance and evaluation.

WE RECOMMEND submitting an application for inclusion in a timetabled (seminar) group to a specific teacheraccording to the instructions from the study department.

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