Czech

Not applicable.

The graduate of the course is capable of:
- understanding principles of neural networks (with backpropagation errors, Hamming, Hopfield, Kohonen network),
- implementing the cluster analysis using the non-hierarchical or hierarchical methods,
- explain the principle of fuzzy inference and approximate reasoning,
- use the predicate logic.

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

Not applicable.

Teaching methods include lectures and computer laboratories. Course is taking advantage of e-learning system. Students have to write a single project/assignment during the course.

Requirements for completion of a course are elaborated by the lecturer responsible for the course every year;
- 30 points can be obtained for activity in the laboratory exercises, consisting in solving tasks (for the procedure for the examination must be obtained at least 15 points)
- 70 points can be obtained for the written exam (the written examination is necessary to obtain at least 35 points)

1. Introduction to Artificial Intelligence.
2. Artificial neural networks, the neuron and its characteristics, the neuron as a classifier.
3. Learning the neuron with binary and real inputs and outputs, single-layer perceptron.
4. Multi-layer feed-forward network, the backpropagation algorithm.
5. Hamming network, Hopfield network, Kohonen network.
6. Cluster analysis, hierarchical cluster analysis.
7. Non-hierarchical cluster analysis, k-means algorithm.
8. Fuzzy sets, fuzzy relations.
9. Logic, fuzzy logic, fuzzy inference, approximate reasoning.
10. Feature methods for classification.
11. The logic of decision-making systems, predicate logic.
12. Inference and substantiation of claims.

Not applicable.

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, the logic of the decision-making systems and predicate logic.

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.

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)