Syllabus
The following list of themas is about interests of lecturers from previous years, but does not limit future themas. Invited are all themas relevant to machine learning and modelling based on data.
Examples of previous themas:
Learning of ruled from data, learning of boolean anf fuzzy rules.
Association and clasification rules.
Inductive inference, inductive logic programming.
Case-based study, transductive inference.
Statistical learning, PAC learning.
Clustering based on similarity, clustering using self-organisation.
Evolutional learning, evolutional extraction of rules from data.
Genetic algorithms, genetic programing.
Evolutional algorithms based on differential evolution and on estimations of distibution.
Learning of artificial neural nets (with and without supervisor).
Perceptrons and multilayered perceptrons.
Neuron nets with radial basis functions.
Self-organising maps, combining of neuron nets and evolution algorithms.
Clasification and regression using support vector machines.
Hierarchical regression models.
Decision trees for classification and regression.
Combining of decision trees to random forests.
General methods of combining classifiers, ensemble methods.
Fuzzy aggregation of classifiers, fuzzy classification.
General methods of combining regression models, reliability of prediction.
Data visualization, visualization of models constructed from data.
Applications of machine learning methods in physics, chemistry, biology and computer games.
Application of models extracted from data in natural and technical sciences.
Annotation
Seminar of machine learning and modelling is oriented to methods of machine learning and modelling based on data. Work of Mgr. and PhD. students, lectures of researchers and occasionally invided lectures of foreign visitors from this area are presented.
We invite also students, which want to refere about some book or paper in area of machine learning or modelling based on data.