Concepts, aims, techniques, environments, and areas of computational intelligence. Uniformed and informed search techniques applied to problems with or without adversaries. Stochastic environment modeling (Markov Decision Processes). Training intelligent agents with reinforcement learning. Basic principles of machine learning: supervised, unsupervised and semi-supervised learning; basic clustering and classification algorithms. Introduction to neural networks. Introduction to deep learning: convolutional and recurrent neural networks. Introduction to deep reinforcement learning. Introduction to genetic algorithms. Introduction to logic programming in Prolog.