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Last update: doc. Dipl.-Math. Erik Jurjen Duintjer Tebbens, Ph.D. (14.09.2013)
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Last update: doc. Dipl.-Math. Erik Jurjen Duintjer Tebbens, Ph.D. (30.05.2018)
The credit test consists of succesful implementation of an exercise in Matlab. The exercise is similar to what has been done during the regular exercises of the course.
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Last update: doc. RNDr. Václav Kučera, Ph.D. (15.01.2019)
ELDEN, L.: Matrix Methods in Data Mining and Pattern Recognition, Fundamentals of Algorithms, 4. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2007.
BJORCK, ÅKE: Numerical Methods for Least Squares Problems. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1996.
HIGHAM, N., STEWART, G. W.: Numerical Linear Algebra in Statistical Computing. The state of the art in numerical analysis (Birmingham, 1986), Inst. Math. Appl. Conf. Ser. New Ser., 9, Oxford Univ. Press, New York, 1987, pp. 41-57.
DUINTJER TEBBENS, J., SCHLESINGER, P.: Improving Implementation of Linear Discriminant Analysis for the High Dimension/Small Sample Size Problem, Computational Statistics and Data Analysis, 2007, vol. 52, no.1, pp. 423-437.
J. KALINA, J. DUINTJER TEBBENS: Metody pro redukci dimenze v mnohorozměrné statistice a jejich výpočet, to appear in the Informacní bulletin of the Czech Statistical Society, in 2014.
J. DUINTJER TEBBENS, I. HNĚTYNKOVÁ, M. PLEŠINGER, Z. STRAKOŠ and P. TICHÝ: Analysis of Methods for Matrix Computations, Basic Methods (in Czech), Matfyzpress Prague, ISBN 978-80-7378-201-6, first edition, 2012, 328 pp.
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Last update: T_KPMS (12.05.2014)
Lecture+exercises. |
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Last update: doc. Dipl.-Math. Erik Jurjen Duintjer Tebbens, Ph.D. (30.05.2018)
The exam is written with most questions multiple-choice questions, except for one or two requiring detailed descriptions. All lectured material will be examinated from.
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Last update: doc. RNDr. Václav Kučera, Ph.D. (19.12.2018)
1. Numerical properties of the SVD and spectral decomposition.
2. PCA and the spectral decomposition.
3. (Multi)-linear regression and the SVD.
4. Dimension reduction in high-dimensional statistics.
5. Pattern recognition and other classification tasks.
6. Nonnegative matrix decompositions.
7. The page ranking problem. |
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Last update: doc. Dipl.-Math. Erik Jurjen Duintjer Tebbens, Ph.D. (30.05.2018)
Only very basic knowledge of linear algebra is required - further knowledge, in particular concerning numerics, is lectured during the course. |