• representation and interpretation of the concept Machine Learning (ML), goals of ML, types of learning, supervised and unsupervised learning, bias, designing of learning system
• ordering of the version space, version space search, search from general to specific, search from specific to general, over-specification, over generation, candidate elimination algorithm
• representation and use of logical conjunctions, inducing logical conjunctions, EGS algorithm, heuristic induction, HGS algorithm
• induction of the disjunction normal form, non-incremental learning using separate and conquer principle (NSC), direct generation of classification rules (AQ11)
• nonincremental generation of clasification decision trees (ID3, C4.5), incremental induction of decision trees (ID5R), trees pruning, processing of unknown values of attributes, classification of numerical attribute, grouping of discrete attribute value, windowing
• nonincremental generation of regretion decision trees (CART), linear versus unlinear data and task
• induction of decision lists, direction with the aid of exceptions (NEX), ordered and non-ordered set of rules (CN2), numeric characteristics of decision rules
• representation and use of threshold concepts, criteria tables, heuristic induction of criteria tables (HCT), linear and spherical threshold units, iterative weight perturbation (IWP)
• representation and use of SVM (Support Vector Machines), solving linear and nonlinear problem, using Kernel funcion
• instance based learning, representation and use of prototypes, instance averaging, induction of competitive disjunctions (NCD, ICD)
• probabilistic representation and classification, induction of naive Bayesian classifiers
• extensional versus intentional concept representation, lazy learning, derivation of the gradient descent rule, locally weighted regression
• acquiring search control knowledge, reinforcement learning, bucket brigade, Q - learning
• unsupervised learning, iterative distance-based clustering, conceptual clustering, CLUSTER/2 algorithm, hierarchical clustering, COBWEB algorithm, structure of taxonomic knowledge, probabilistic clustering
• computational learning theory, probably learning and approximately correct hypothesis, error of a hypothesis, PAC learn-ability, sample complexity for finite and infinite hypothesis spaces, Vapnik-Chervonenkis dimension
• basic principles of machine learning, cognitive algorithm design, improvement of the classification algorithm results (bagging and boosting methods)
• learnig using set of methods, final decision by application of voting, stacking, bagging, boosting, random forests (RF)
• application areas of machine learning, decreasing of the internet users cognitive load, opinion classification, antisocial behaviour recognition (fake news, toxic posts), active learning

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