Machine Learning

Representation and interpretation of the concept Machine Learning (ML), goals of ML, types of learning, supervised and unsupervised learning, bias
Probabilistic representation and classification, induction of naive Bayesian classifiers, measures of effectiveness of models
Nonincremental generation of clasification decision trees (ID3, C4.5), nonincremental generation of regression decision trees (CART), linear versus unlinear data and task
Learnig using the set of methods, final decision by applying of voting, bagging, boosting, random forests (RF)
Regression analysis, linear regression, logistic regression
Lazy learning, extensional versus intentional concept representation, algorithm k-NN
Representation and use of threshold concepts, linear and spherical threshold units, iterative weight perturbation (IWP), perceptron
Representation and use of SVM (Support Vector Machines), solving linear and nonlinear problem, using Kernel funcion
Deep neural networks, reccurent neural networks, LSTM, GRU, Transformer, attention mechanism, family of BERT, Family of T5, GPT