Mathematics for Knowledge Processing
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | Czech |
| Code | 460-4066/01 |
| Abbreviation | MPZZ |
| Course title | Mathematics for Knowledge Processing |
| Credits | 6 |
| Coordinating department | Department of Computer Science |
| Course coordinator | doc. Mgr. Pavla Dráždilová, Ph.D. |
Osnova předmětu
Relations and their properties. Ordering, equivalence, tolerance
Algebra, operations, morphisms and congruences
Graphs and hypergraphs
Ordered sets
Lattices and other algebras with two operations
Conceptual lattices
Association rules
Rough sets and fuzzy sets
Metrics, ultrametrics and dissimilarities
Metric and topological spaces
Dimension and curse of dimensionality
Clustering I
Clustering II, quality of clustering
Algebra, operations, morphisms and congruences
Graphs and hypergraphs
Ordered sets
Lattices and other algebras with two operations
Conceptual lattices
Association rules
Rough sets and fuzzy sets
Metrics, ultrametrics and dissimilarities
Metric and topological spaces
Dimension and curse of dimensionality
Clustering I
Clustering II, quality of clustering
E-learning
Study supports are available in the LMS for course students.
Povinná literatura
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.
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.
Doporučená literatura
1. Eldén, L., Matrix Methods in Data Mining and Pattern Recognition, SIAM 2007.