Introduction to Artificial Intelligence, Information Theory, systems and models.

Graph algorithms: vertices, edges, edge weighting, basic theory and graph classification.

Dijkstra’s algorithm, A*, theory and motivation, algorithm description and pseudocode.

Min-max algorithm, Alpha-Beta pruning algorithm, theory and motivation, pseudocode, algorithm description and graphical illustration.

Negascout and its difference from alternative algorithms. Pseudocode and algorithm description. Graphical illustration and practical example.

Monte Carlo tree search algorithm, Monte Carlo algorithm itself. Algorithm description and pseudocode. Graphical illustration and practical calculation.

Neural networks, explanation of the basic components of neural networks (activation functions, neurons, layers, etc.), basic parameter estimation using backpropagation and derivation of parameter estimates in a single-layer network.

Decision-making and Decision Trees, Behavioural Trees, explanation of the basic elements, examples of applications.

Learning process and parameter estimation, Reinforcement learning and alternative methods.

The issue of pseudo-random numbers, examples of algorithms (and illustrative computation), testing the quality of random number generators.

Markov chains, statistical theory, examples of use.

Naive Bayes method, statistical theory, examples of use.

The issue of data generation and examples of use.

Transposition tables and memory requirements of algorithms, illustrative examples and practical applications.

Statistical evaluation and evaluation functions.