157-9581/01 – Knowledge discovery from data (VZe)

Gurantor departmentDepartment of Systems EngineeringCredits10
Subject guarantordoc. Dr. Ing. Miroslav HudecSubject version guarantordoc. Dr. Ing. Miroslav Hudec
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2020/2021Year of cancellation
Intended for the facultiesEKFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
HUD0118 doc. Dr. Ing. Miroslav Hudec
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Part-time Examination 28+0

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:

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. AGGRAWAL, Charu. Data Mining: The Textbook. Cham: Springer, 2015. ISBN 978-3-319-14141-1.

Recommended literature:

EBOCH, M. M. Data mining. New York, Greenhaven Publishing. 2018. ISBN 781534501966. HAN, Jiawei. Data mining: concepts and techniques. Haryana, India: Elsevier, 2012. BERKA, Petr. Dobývání znalostí z databází. Praha: Academia, 2003. ISBN 80-200-1062-9.

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

Full-time form (validity from: 2020/2021 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (P0311D050020) Systems Engineering and Informatics K English Ostrava Choice-compulsory type B study plan
2020/2021 (P0311D050020) Systems Engineering and Informatics P English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner