State exam contents

ZNA01 Basic concepts of Formal Concept Analysis.

ZNA02 Basic algorithms of Formal Concept Analysis.

NEU03 Feedforward neural networks and the backpropagation method.

NEU04 Convolution and Convolutional Neural Networks.

NEU05 Deep Neural Networks (DNN) and their use.

NEU06 Graph Neural Networks (GNN) and their applications.

STU07 Phases of machine learning, overfitting of machine learning models.

STU08 Decision trees, random forests, and other types of ensemble models of machine learning.

STU09 Support Vector Machines (SVM) and other classification methods.

ZNA10 Basic concepts of fuzzy logic and their use in formal concept analysis.

VSM11 Solving systems of linear equations, calculating eigenvalues and eigenvectors of matrices.

VSM12 General methods of generating random numbers.

VSM13 Stochastic processes and the MCMC method.

VSM14 Principles and methods of cluster analysis.

VSM15 Principal Component Analysis (PCA), Factor Analysis.

KOA16 Polynomial and NP-complete problems of combinatorial optimization.

KOA17 Optimal acyclic subgraphs (paths, trees, matchings).

KOA18 Network flows (algorithms, complexity, modifications, and applications).

NPR16 Stochastic processes and their properties: Concepts of stationarity, causality, invertibility, and the time-domain approach.

NPR17 Time series analysis in the frequency domain: Spectral density and the periodogram.

NPR18 Time series prediction: Properties of predictors and forecasting models.