Terminated in academic year 2024/2025
Knowledge mining
| Type of study | Doctoral |
|---|---|
| Language of instruction | Czech |
| Code | 157-9981/01 |
| Abbreviation | VZ |
| Course title | Knowledge mining |
| Credits | 10 |
| Coordinating department | Department of Systems Engineering and Informatics |
| Course coordinator | doc. Dr. Ing. Miroslav Hudec |
Subject syllabus
1. Introduction into knowledge discovery (definition, relation to the other scientific disciplines, basic concepts).
2. Data types (numeric, categorical, text, fuzzy data, mixed data types). Logical and statistical view on data and on interpreting knowledge
3. Steps of knowledge discovery: data pre-processing, data cleaning, mining and interpreting results.
4. Correlation and causality, functional and flexible functional dependencies.
5. Computational intelligence in knowledge discovery from the data.
6. Classification, association rules, decision trees.
7. Statistical and logical data summaries
8. Data visualization.
9. Mining knowledge from time series.
10. Machine learning in knowledge discovery (types of learning and their properties, data, evaluation of results).
2. Data types (numeric, categorical, text, fuzzy data, mixed data types). Logical and statistical view on data and on interpreting knowledge
3. Steps of knowledge discovery: data pre-processing, data cleaning, mining and interpreting results.
4. Correlation and causality, functional and flexible functional dependencies.
5. Computational intelligence in knowledge discovery from the data.
6. Classification, association rules, decision trees.
7. Statistical and logical data summaries
8. Data visualization.
9. Mining knowledge from time series.
10. Machine learning in knowledge discovery (types of learning and their properties, data, evaluation of results).
E-learning
Students have all relevant presentations from lectures and instructions in LMS Moodle
Literature
SKANSI, Sandro. Introduction to Deep Learning. Cham: Springer, 2018. ISBN978-3-319-73003-5 .
HUDEC, Miroslav. Fuzziness in Information Systems - How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization. Cham: Springer, 2016. ISBN 978-3-319-42516-0.
HUDEC, Miroslav. Fuzziness in Information Systems - How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization. Cham: Springer, 2016. ISBN 978-3-319-42516-0.