460-4066/03 – Mathematics for Knowledge Processing (MPZZ)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantorprof. RNDr. Václav Snášel, CSc.Subject version guarantorMgr. Pavla Dráždilová, Ph.D.
Study levelundergraduate or graduate
Study languageCzech
Year of introduction2015/2016Year of cancellation
Intended for the facultiesUSP, FEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
SNE10 Mgr. Pavla Dráždilová, Ph.D.
MEN059 Mgr. Marek Menšík, Ph.D.
SNA57 prof. RNDr. Václav Snášel, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 2+2

Subject aims expressed by acquired skills and competences

Graduate Course gives the following knowledge and skills: basic theoretical background for data analysis, implementation and application of selected methods.

Teaching methods



The course provides the students with basic mathematical methods for data analysis. Lectures provide the students the teoretical backgroud for independent work. Tutorials offer space for discussing the issues, problem solution demonstration and illustrative examples exercising.

Compulsory literature:

Dan A Simovici; Chabane Djeraba. Mathematical tools for data mining : set theory, partial orders, combinatorics. Springer, 2008. David Skillicorn. Understanding Complex Datasets: Data Mining with Matrix Decompositions, Chapman & Hall, 2007. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer; Corr. 3rd edition, 2009.

Recommended literature:

Eldén, L., Matrix Methods in Data Mining and Pattern Recognition, SIAM 2007.

Way of continuous check of knowledge in the course of semester


Další požadavky na studenta

Additional requirements are not placed on the student.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1) Algebras 2) Graphs and Hypergraphs 3) Partial Ordered sets 4) Lattices and Boolean Algebras 5) Conceptual lattice 6) Topology 7) Frequent Item Sets and Association Rules 8) Rough Sets 9) Approximation Spaces, 10) Dissimilarities, Metrics, and Ultrametrics 11) Dimensions and The Dimensionality Curse 12) Clustering 13) Combinatorics, Vapnik-Chervonenkis Dimension

Conditions for subject completion

Full-time form (validity from: 2016/2017 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination 100  51
Mandatory attendence parzicipation: obligatory participation in all exercises, 3 apologies are accepted

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2018/2019 (N2658) Computational Sciences (2612T078) Computational Sciences P Czech Ostrava 2 Choice-compulsory study plan
2017/2018 (N2658) Computational Sciences (2612T078) Computational Sciences P Czech Ostrava 2 Choice-compulsory study plan
2016/2017 (N2658) Computational Sciences (2612T078) Computational Sciences P Czech Ostrava 2 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner