Czech

Not applicable.

The knowledge on the Bachelor´s degree level is requested, namely on numerical mathematics. The laboratory work is expected knowledge of Matlab programming environment.

Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
Basically:
- obligatory computer-lab tutorial (missed labs must be properly excused and can be replaced after agreement with the teacher)
- voluntary lecture.

Gaining knowledge about artificial neural networks, introduction to Hierarchical and non-hierarchical cluster analysis. Introduction to the theory of fuzzy sets, fuzzy relations, fuzzy logic, fuzzy inference methods as approximate reasoning. Furthermore, methods for relevant features selection and for evaluation of the results obtained using above approaches.
The graduate of the course is capable of:
- understanding principles of neural networks (with backpropagation errors, Hamming, Kohonen network),
- implementing the cluster analysis using the non-hierarchical or hierarchical methods,
- explaining the principle of fuzzy inference and approximate reasoning,
- performing selection of the most relevant features for further analysis,
- evaluating the performance of machine learning algorithms,
- giving examples of biomedical areas where above methods are widely used.

Not applicable.

Not applicable.

Kozumplík, J., Provazník, I.: Umělá inteligence v medicíně. Elektronická skripta. ÚBMI FEKT VUT v Brně, Brno, 2007. (CS)

Šnorek, M.: Neuronové sítě a neuropočítače. Skripta ČVUT, Praha, 2002 (CS)