157-9581/01 – Knowledge discovery from data (VZe)
| Gurantor department | Department of Systems Engineering | Credits | 10 |
| Subject guarantor | doc. Dr. Ing. Miroslav Hudec | Subject version guarantor | doc. Dr. Ing. Miroslav Hudec |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2020/2021 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The aim of the course is to understand and master the main approaches and methods for knowledge discovery from the heterogeneous data and interpreting knowledge in the understandable way for diverse domain experts’ categories. The lectures provide a theoretical basis for understanding knowledge discovery, whereas the seminars provide space for demonstrating tasks, examining various cases and discussion.
Teaching methods
Lectures
Seminars
Individual consultations
Summary
The aim of the course is to understand and master the main approaches and methods for knowledge discovery from the heterogeneous data and interpreting knowledge in the understandable way for diverse domain experts’ categories. The lectures provide a theoretical basis for understanding knowledge discovery, whereas the seminars provide space for demonstrating tasks, examining various cases and discussion.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Student presents and defends seminar work as a first part of the exam. The second part of the exam is discussion of the given topic raised by lecturer.
E-learning
Students have all relevant presentations from lectures and instructions in LMS Moodle
Other requirements
Seminar work according to the expected structure and content.
Obtained more than 50% of the total points on exam.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
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 vizualization.
9. Mining knowledge from time series.
10. Machine learning in knowledge discovery (types of learning and their properties, data, evaluation of results).
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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