1. Overview of machine learning tasks: classification, regression, prediction and areas of application 2. Decision trees: constructions, metrics 3. - 4. Support vector method (SVM): formulation, soft margin, solution, use of cores, core trick, multiclass classification 5. Introduction to neural networks: types of networks and their architecture, types of learning 6. - 7. Multilayer perceptron (MLP) networks: perceptron, activation functions and their types, layers and learning, relearning, regularization 8. Reinforcement learning: use and overview of algorithms with a focus on Q-learning 9. Metaalgorithms of machine learning: grouping of weak classifiers, random forests, boost, AdaBoost 10. Fuzzy set: introduction, properties (value range, height, carrier, core), sections, affiliation 11. Fuzzy sets and their extensions: overview of set and propositional operations and their properties, fuzzy numbers and fuzzy relations 12. Application of fuzzy logic: process of fuzzification and defuzzification, linguistic variables 13. Bayesian statistics: definition of basic terms (distribution, a priori, posterior), Bayesian theorem and its use 14. Bayesian networks (BN): graph representation, model probability distribution (string rule), overview of BN learning algorithms
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