B_ZZD Information Retrieval from Database

University of Finance and Administration
Summer 2014
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: Dagmar Medová, DiS.
Timetable of Seminar Groups
B_ZZD/pAPH: Mon 8:45–9:29 DELL ROOM E302PC, Mon 9:30–10:15 DELL ROOM E302PC, P. Berka
B_ZZD/vAPH: Fri 28. 3. 17:15–18:45 E128, Fri 11. 4. 15:30–17:00 E128, 17:15–18:45 E128, P. Berka
Prerequisites
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.
General note: Aa2.
Information on the extent and intensity of the course: 6 hodin KS/semestr.
Teacher's information
http://sorry.vse.cz/~berka/B_ZZD
The course is also listed under the following terms Winter 2007, Summer 2009, Summer 2010, Summer 2011, Winter 2011, summer 2012, Summer 2013, Summer 2015, Summer 2016, Summer 2017, Summer 2018, Summer 2019, Summer 2020, Summer 2021.
  • Enrolment Statistics (Summer 2014, recent)
  • Permalink: https://is.vsfs.cz/course/vsfs/summer2014/B_ZZD