Statistical Pattern Recognition and Decision Making Methods

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Code Completion Credits Range Language
18SMRR ZK 2 2P+0C Czech
Garant předmětu:
Jaromír Kukal
Jaromír Kukal
Jaromír Kukal
Department of Software Engineering

Collection of recognition and classification methods with accent to mathematical and statistical principles of their design

and functionality.

Syllabus of lectures:

1.Introduction - what is pattern recognition and decision making

2.Statistical (feature-based) and structural (syntactic) pattern recognition

3.Introduction to statistical pattern recognition - supervised and non-supervised classifiers

4.Simple metric classifiers - NN classifier, k-NN classifier, linear classifier

5.Bayesian classifier - the basic principle, parametric and non-parametric B.c., B.c. for normally distributed classes, parameter estimation, necessary conditions of linearity, special cases in two dimensions

6.Non-metric classifiers, decision trees

7.Non-supervised classifiers - cluster analysis in the feature space, iterative and hierarchical methods, criteria of cluster separability

8.k-means iterative algorithm and its modifications

9.Agglomerative hierarchical clustering, inter-cluster metrics, stop conditions, estimating the number of clusters

10.Dimensionality reduction of the feature space, feature extraction and selection, class separability criteria, Mahalanobis distance

11.Principal component transform

12.Optimal and sub-optimal feature selection methods, sequential and floating search

13.Decision making as a discrete optimization problem

14.Basic methods for unconstrained and constrained discrete optimization

Syllabus of tutorials:
Study Objective:
Study materials:

Key references:

[1] Urbanowicz, R. J. J., Browne, W. N. Introduction to Learning Classifier Systems. Berlin: Springer, 2017.

[2] Matloff, N. Statistical Regression and Classification: From Linear Models to Machine Learning. Boca Raton: CRC

press, 2017.

Recommended references:

[3] Duda, R. O., Hart, P. E., Stork, D. G. Pattern Classification. 2nd edition. New York: Willey, 2007.

[4] Scholkopf, B., Smola, A. J. Learning with Kernels. Cambridge: MIT Press, 2001.

[5] Aggarwal, Ch. C. Data Mining: The Textbook. Cham (Switzerland): Springer, 2015.

[6] Izenman, A. J. Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning.

Corr. 2nd printing 2013 edition. New York: Springer, 2013.

[7] Proceedings of the International Workshop on Multiple Classifier Systems (MCS).

Time-table for winter semester 2023/2024:
Time-table is not available yet
Time-table for summer semester 2023/2024:
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The course is a part of the following study plans:
Data valid to 2023-08-30
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