Artificial intelligence, basic aproaches, methods.
Machine learning, review of machine learning tasks. Model complexity, loss function, dimenzionality.
Spatial aspects – spatial constinuity, stacionarity, spatial sampling, bootstrapping.
Introduction to classification. Naive Bayes classification. K-means neighbors algorithm.
Decision trees. Selection of attributes using entropy, frequency tables, Gini index. Evaluation of classification accuracy.
Support vector machines, regression with SVM (SVR). Discrimination analysis
Neural networks, multilayer perceptron, regression neural networks, probable neural networks, Kohonen maps, radial function, deep learing, convolutional neural network.
Bayes networks. Bagging, boosting, stacking. Model tuning, model validation
Data mining, data science. Data mining methodology. Pattern mining, sequences. Association rules learning. Text mining. Text preprocessing. Information lift. Weight normalisation.
Logistic regression, symbolic regression, qunatile regression, robust regression
Cluster analysis, hierarchical and nonhierarchical clustering, association rules, density clusters
Data mining from data streams
Model dynamics and dynamic basics. Chaos – tranzitivity. Chaos detection in geography.
Fractals. Fractal dimension and its estimation using selected algorithms.
Fractal clustering, self affine fractals and multifractals
Machine learning, review of machine learning tasks. Model complexity, loss function, dimenzionality.
Spatial aspects – spatial constinuity, stacionarity, spatial sampling, bootstrapping.
Introduction to classification. Naive Bayes classification. K-means neighbors algorithm.
Decision trees. Selection of attributes using entropy, frequency tables, Gini index. Evaluation of classification accuracy.
Support vector machines, regression with SVM (SVR). Discrimination analysis
Neural networks, multilayer perceptron, regression neural networks, probable neural networks, Kohonen maps, radial function, deep learing, convolutional neural network.
Bayes networks. Bagging, boosting, stacking. Model tuning, model validation
Data mining, data science. Data mining methodology. Pattern mining, sequences. Association rules learning. Text mining. Text preprocessing. Information lift. Weight normalisation.
Logistic regression, symbolic regression, qunatile regression, robust regression
Cluster analysis, hierarchical and nonhierarchical clustering, association rules, density clusters
Data mining from data streams
Model dynamics and dynamic basics. Chaos – tranzitivity. Chaos detection in geography.
Fractals. Fractal dimension and its estimation using selected algorithms.
Fractal clustering, self affine fractals and multifractals