Introduction to Arificiall Intelligence. Categories of Artificial intelligence, supervised, unsupervised and semi-supervised learning methods. Methods with or without modelling. Probabilistic methods. Intelligent Agents.
Introduction to artificial intelligence methods without modeling (stateless - Supervised learning). The structure of the simple preceptor, neuron. The structure of neural networks. The method of backpropagation. Deep learning neural networks. Methods of nonlinear categorization.
Introduction to model-free artificial intelligence methods (stateless - Unsupervised learning). Classification methods, k-means, DBSCAN, spectral clustering. Unsupervised learning methods using training ( autoencoders, stacked autoencoders, deep learning).
Introduction to artificial intelligence methods with modeling (state modeling - deterministic). Introduction to Search Problem Modeling. Search trees. Euphemistic Methods. Local Search Algorithms and Optimization Methods. Search by width, depth. Uniform Cost Search. A Star A Star Relaxations.
Introduction to artificial intelligence methods with modeling (state modeling - competitive). Competitive Methods. Game Theory, Max Min Algorithms, ExpectMax Algorithms, Alpha-Beta pruning. Adversarial Generative Networks (GANs) and deep learning
