Mathematics for Computer Science
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | English |
| Code | 460-4164/02 |
| Abbreviation | MPI |
| Course title | Mathematics for Computer Science |
| Credits | 5 |
| Coordinating department | Department of Computer Science |
| Course coordinator | doc. Mgr. Pavla Dráždilová, Ph.D. |
Subject syllabus
- Lattices and other algebras with two operations.
- Concept lattices and association rules.
- Rough sets and fuzzy sets.
- Metrics, ultrametrics, dissimilarities, and similarities.
- Metric and topological spaces.
- Dimensionality and the curse of dimensionality.
- Eigenvalues, eigenvectors, PCA, SVD.
- Mathematical foundations of clustering algorithms.
- Clustering quality assessment.
- Entropy, Kullback–Leibler divergence, mutual information, coding limits, and code sets.
- Variable-length codes and statistical coding.
- Fourier transform.
- Wavelet transform and convolution.
- Concept lattices and association rules.
- Rough sets and fuzzy sets.
- Metrics, ultrametrics, dissimilarities, and similarities.
- Metric and topological spaces.
- Dimensionality and the curse of dimensionality.
- Eigenvalues, eigenvectors, PCA, SVD.
- Mathematical foundations of clustering algorithms.
- Clustering quality assessment.
- Entropy, Kullback–Leibler divergence, mutual information, coding limits, and code sets.
- Variable-length codes and statistical coding.
- Fourier transform.
- Wavelet transform and convolution.
E-learning
Study materials are available to course students in LMS.
Literature
1. Simovici, Dan A., and Chabane Djeraba. "Mathematical tools for data mining." SpringerVerlag, London (2008).
2. Cover, Thomas M. Elements of information theory. John Wiley & Sons, 1999.
2. Cover, Thomas M. Elements of information theory. John Wiley & Sons, 1999.
Advised literature
1. Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020.