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Mathematics for Knowledge Processing

Summary

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.

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.


Language of instruction čeština, angličtina, čeština, angličtina
Code 460-4066
Abbreviation MPZZ
Course title Mathematics for Knowledge Processing
Coordinating department Department of Computer Science
Course coordinator doc. Mgr. Pavla Dráždilová, Ph.D.