Machine Learning 1

Course guarantor:

Jan Flusser

Lecturer:

Jan Flusser

Tutor:

Jan Flusser, Adam Novozámský

Supervisor:

Department of Mathematics

Synopsis:

[1] features for description and recognition of 2-D shapes

[2] invariant features, Fourier descriptors, moment invariants, differential invariants

[3] statistical pattern recognition, supervised and nonsupervised classification, NN- classifier, linear classifier, Bayessian classifier

[4] clustering in a feature space, iterative and hierarchical methods

[5] dimensionality reduction of a feature space

Requirements:

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Study materials:

Key references:

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

[2] J. Flusser, T. Suk, B. Zitova: 2D and 3D Image Analysis by Moments 1st Edition, Wiley &Sons Ltd., 2016

Recommended references:

[3] Pratt W. K.: Digital Image Processing (3rd ed.), John Wiley, New York, 2001

[4] J. Flusser, T. Suk, B. Zitová, Moments and Moment Invariants in Pattern Recognition, Wiley &Sons Ltd., 2009

Note:

Time-table for winter semester 2025/2026:

Time-table is not available yet

Time-table for summer semester 2025/2026:

Time-table is not available yet

The course is a part of the following study plans: