VSFS:B_ZZD Knowledge Discovery - Course Information
B_ZZD Knowledge Discovery in Database
University of Finance and AdministrationSummer 2016
- Extent and Intensity
- 2/0. 3 credit(s). Type of Completion: z (credit).
- Teacher(s)
- 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: Ing. Barbora Ptáčková - Timetable of Seminar Groups
- B_ZZD/pAPH: Mon 8:45–9:29 E305, Mon 9:30–10:15 E305, except Mon 8. 2., except Mon 7. 3. ; and Thu 17. 3. 8:45–10:15 E306, 10:30–12:00 E306, P. Berka
B_ZZD/vAPH: Fri 26. 2. 15:30–17:00 E128, Fri 8. 4. 15:30–17:00 E228, 17:15–18:45 E228, P. Berka - Prerequisites
- B_ES Expert Systems
There are no prerequisites for this course. - 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.
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, - Syllabus
- 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
- Literature
- Berka,P.Dobývání znalostí z databází. Academia, Praha 2003. ISBN 80-200-1062-9.
- 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. Second edition. Morgan Kaufmann, San Francisco 2005
- 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
- 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: 6 hodin KS/semestr.
- Enrolment Statistics (Summer 2016, recent)
- Permalink: https://is.vsfs.cz/course/vsfs/summer2016/B_ZZD