1) Artificial intelligence, basic aproaches, methods.
2) Machine learning, basic concepts, supervised learning, unsupervised learning, backpropagation, hybrid methods. Review of machine learning tasks. Model complexity, loss function, dimenzionality.
3) Spatial aspects – spatial constinuity, stacionarity, spatial sampling, bootstrapping. Preparation of analysis– analysis of sample network, Morisita diagram, data transformation.
4) Introduction to classification. Naive Bayes classification. K-means neighbors algorithm.
5) Decision trees. Selection of attributes using entropy, frequency tables, Gini index. Evaluation of classification accuracy.
6) Support vector machines, regression with SVM (SVR).
7) Neural networks, multilayer perceptron, regression neural networks, probable neural networks, Kohonen maps, radial function, deep learing, convolutional neural network.
8) Data mining, data science. Data mining methodology. Pattern mining, sequences. Association rules learning..
9) Text mining. Text preprocessing. Information lift. Weight normalisation.
10) Cluster analysis, hierarchical and nonhierarchical clustering, association rules, density clusters
11) Introduction to chaos and fractals theories. Model dynamics and dynamic basics. Chaos detection in geography.
12) Fractals. Fractal dimension and its estimation using selected algorithms. Application in geoinformatics
13) Introduction to genetic programming. Sworm intelligence.
2) Machine learning, basic concepts, supervised learning, unsupervised learning, backpropagation, hybrid methods. Review of machine learning tasks. Model complexity, loss function, dimenzionality.
3) Spatial aspects – spatial constinuity, stacionarity, spatial sampling, bootstrapping. Preparation of analysis– analysis of sample network, Morisita diagram, data transformation.
4) Introduction to classification. Naive Bayes classification. K-means neighbors algorithm.
5) Decision trees. Selection of attributes using entropy, frequency tables, Gini index. Evaluation of classification accuracy.
6) Support vector machines, regression with SVM (SVR).
7) Neural networks, multilayer perceptron, regression neural networks, probable neural networks, Kohonen maps, radial function, deep learing, convolutional neural network.
8) Data mining, data science. Data mining methodology. Pattern mining, sequences. Association rules learning..
9) Text mining. Text preprocessing. Information lift. Weight normalisation.
10) Cluster analysis, hierarchical and nonhierarchical clustering, association rules, density clusters
11) Introduction to chaos and fractals theories. Model dynamics and dynamic basics. Chaos detection in geography.
12) Fractals. Fractal dimension and its estimation using selected algorithms. Application in geoinformatics
13) Introduction to genetic programming. Sworm intelligence.