N_DZD Data Mining

University of Finance and Administration
Summer 2022
Extent and Intensity
2/0/0. 5 credit(s). Type of Completion: zk (examination).
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: Ivana Plačková
Timetable of Seminar Groups
N_DZD/vAPH: Fri 25. 2. 14:00–15:30 S24, 15:45–17:15 S24, Fri 25. 3. 14:00–15:30 S24, Fri 29. 4. 17:30–19:00 E224, 19:15–20:45 E224, Sat 30. 4. 9:45–11:15 E307, 11:30–13:00 E307, 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. The lectures focus on different 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 the basic principles of various data mining algorithms, • understand the basic methods of evaluation of created models, • understand basic preprocessing operations, • formulate data mining and text mining 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, SVM 8. bayesian methods, instance based learning 9. Evaluation what has been learned 10. Data preprocessing methods 11. Data mining using Rapid Miner 12. Text mining
Literature
    required literature
  • BERKA, Petr. Dobývání znalostí z databází (Knowledge Discovery in Databases). Praha: Academia, 2003, 370 pp. ISBN 80-200-1062-9. info
    recommended literature
  • Witten I., Frank E.: Data Mining, Practical Machine Learning Tools and Techniques with Java. Fourth edition. Morgan Kaufmann, San Francisco 2016
  • HAN, J. - KAMBER, M. Data mining: concepts and techniques. Morgan Kaufmann, 2011
Teaching methods
- lectures and seminars in full-time study, - tutorials in part-time study, - case study of data analysis using data mining system
Assessment methods
- written final test consisting of unstructured questions (min 60% points) - case study of data analysis using data mining system
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: 14 hodin KS/semestr.
The course is also listed under the following terms Summer 2020, Summer 2021, Summer 2023, Summer 2024, Summer 2025.
  • Enrolment Statistics (Summer 2022, recent)
  • Permalink: https://is.vsfs.cz/course/vsfs/summer2022/N_DZD