460-4066/02 – Mathematics for Knowledge Processing (MPZZ)
Gurantor department | Department of Computer Science | Credits | 6 |
Subject guarantor | Mgr. Pavla Dráždilová, Ph.D. | Subject version guarantor | Mgr. Pavla Dráždilová, Ph.D. |
Study level | undergraduate or graduate | Requirement | Compulsory |
Year | 1 | Semester | winter |
| | Study language | English |
Year of introduction | 2015/2016 | Year of cancellation | |
Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Graduate Course gives the following knowledge and skills:
basic theoretical background for data analysis,
implementation and application of selected methods.
Teaching methods
Lectures
Tutorials
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.
Compulsory 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.
Recommended literature:
1. Eldén, L., Matrix Methods in Data Mining and Pattern Recognition, SIAM 2007.
Way of continuous check of knowledge in the course of semester
3 on-line tests during semester and a written test on the end of semester. The course is completed by an examination consisting of a written and oral part. The written part is compulsory and the oral part is optional.
E-learning
Other requirements
Additional requirements are not placed on the student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1) Algebras
2) Graphs and Hypergraphs
3) Partial Ordered sets
4) Lattices and Boolean Algebras
5) Conceptual lattice
6) Topology
7) Frequent Item Sets and Association Rules
8) Rough Sets
9) Approximation Spaces,
10) Dissimilarities, Metrics, and Ultrametrics
11) Dimensions and The Dimensionality Curse
12) Clustering
13) Quality of clustering
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction