BA_KDD Knowledge Discovery in Databases

University of Finance and Administration
Winter 2021
Extent and Intensity
0/2/0. 3 credit(s). Recommended Type of Completion: z (credit). Other types 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
Supplier department: Department of Computer Science and Mathematics – Departments – University of Finance and Administration
Timetable of Seminar Groups
BA_KDD/cCUPH: each even Monday 15:45–16:29 E305, each even Monday 16:30–17:15 E305, each even Monday 17:30–18:14 E305, each even Monday 18:15–19:00 E305, P. Berka
BA_KDD/cECPH: each odd Monday 15:45–16:29 E307, each odd Monday 16:30–17:15 E307, each odd Monday 17:30–18:14 E307, each odd Monday 18:15–19:00 E307, P. Berka
Prerequisites
no special prerequisities
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
Syllabus
  • - Data, information, knowledge
  • - Process of knowledge discovery in databases
  • - Machine learning
  • - Weka
  • - Symbolic classification and prediction
  • - Subsymbolic classification and prediction
  • - Dependency analysis
  • - Segmentation
  • - Evaluation of created models
  • - New trends
  • - Real tasks
  • - Presentations of case studies
Literature
    required literature
  • Witten I., Frank E.: Data Mining, Practical Machine Learning Tools and Techniques with Java. Fourth edition. Morgan Kaufmann, San Francisco 2016
    recommended literature
  • 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 system
Assessment methods
- written final test consisting of unstructured questions (min 50% points)
- case study of data analysis using data mining system
Language of instruction
English
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.
The course is also listed under the following terms Winter 2020, Winter 2022, Winter 2023, Winter 2024.
  • Enrolment Statistics (Winter 2021, recent)
  • Permalink: https://is.vsfs.cz/course/vsfs/winter2021/BA_KDD