| Course Unit Code | 460-4066/01 |
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| Number of ECTS Credits Allocated | 6 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | There are no prerequisites or co-requisites for this course unit |
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| Name of Lecturer(s) | Personal ID | Name |
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| SNE10 | Mgr. Pavla Dráždilová, Ph.D. |
| Summary |
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| 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. |
| Learning Outcomes of the Course Unit |
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Graduate Course gives the following knowledge and skills:
basic theoretical background for data analysis,
implementation and application of selected methods. |
| Course Contents |
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1) Relations and their properties. Ordering, equivalence, tolerance
2) Algebra, operations, morphisms and congruences
3) Graphs and hypergraphs
4) Ordered sets
5) Lattices and other algebras with two operations
6) Conceptual lattices
7) Association rules
8) Rough sets and fuzzy sets
9) Metrics, ultrametrics and dissimilarities
10) Metric and topological spaces
11) Dimension and curse of dimensionality
12) Clustering I
13) Clustering II, quality of clustering
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| Recommended or Required Reading |
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| Required Reading: |
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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. |
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.
3. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer; Corr. 3rd edition, 2009.
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| Recommended Reading: |
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| 1. Eldén, L., Matrix Methods in Data Mining and Pattern Recognition, SIAM 2007. |
| 1. Eldén, L., Matrix Methods in Data Mining and Pattern Recognition, SIAM 2007. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 40 (40) | 20 |
| Credit test | Written test | 19 | 10 |
| Online tests | Written test | 21 | 0 |
| Examination | Examination | 60 (60) | 20 |
| Test | Written examination | 40 | 20 |
| Oral examination | Oral examination | 20 | 0 |