Mathematics for Knowledge Processing

Type of study	Follow-up Master
Language of instruction	English
Code	460-4066/02
Abbreviation	MPZZ
Course title	Mathematics for Knowledge Processing
Credits	6
Coordinating department	Department of Computer Science
Course coordinator	doc. Mgr. Pavla Dráždilová, Ph.D.

Subject syllabus

Algebras
Graphs and Hypergraphs
Partial Ordered sets
Lattices and Boolean Algebras
Conceptual lattice
Topology
Frequent Item Sets and Association Rules
Rough Sets
Approximation Spaces,
Dissimilarities, Metrics, and Ultrametrics
Dimensions and The Dimensionality Curse
Clustering
Quality of clustering

E-learning

Study materials are available in MS Teams for students of the course.

Literature

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

Advised literature

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

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