Statistical pattern recognition and decision making methods

Lecturer:

Jan Flusser (gar.)

Tutor:

Jan Flusser (gar.)

Supervisor:

Department of Software Engineering in Economy

Synopsis:

The course is devoted to the pattern recognition and decision-making methods which work with statistical data. Applications in economy and other areas are presented.

Requirements:

Unable to write along with the Master study ROZ2

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] Duda R.O. et al., Pattern Classification, (2nd ed.), John Wiley, New York, 2001

Recommended references:

[2] Philip E. Gill, Walter Murray, and Margaret H. Wright, Practical Optimization, Academic Press, 1981

Note:

Time-table for winter semester 2011/2012:

Time-table is not available yet

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans: