Czech

Not applicable.

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.

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 solve two tests (in the middle and at the end of semester, during the laboratories).

Requirements for completion of a course are elaborated every year by the lecturer responsible for the course.

- Total 25 points can be obtained for team project (non-zero point must be obtained)
- Total 75 points can be obtained for the exam (at least 12 points must be obtained from each of three parts of the exam)
- Total 50 points (for team project and the exam) must be obtained to pass the course

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, 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. Features selection and decorrelation.
11. Evaluation of classification, prediction and approximation algorithms.
12. Biomedical applications of machine learning tools.

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, methods for relevant features selection and for evaluation of the results obtained using above approaches.

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)