Electronics Engineering

Introduction to Machine Learning and data driven modelling. Soft Computing, Computational Intelligence. Basic data driven modelling problems: clustering, classification, unsupervised modelling, function approximation, prediction. Generalization capability. Deduction and induction. Induction inference principle over normed spaces. Models and training algorithms. Distance measures and basic preprocessing procedures. Optimization problems. Optimality conditions. Linear regression. LSE and RLSE algorithms. Numerical optimization algorithms: steepest descent and Newton’s method. Fuzzy logic principles. Fuzzy induction inference principle. Fuzzy Rules. Classification systems: performance and sensitivity measures. K-NN Classification rule. The biological neuron and the central nervous system. Perceptron. Feedforward networks: Multi-layer perceptron. Error Back Propagation algorithm. Support Vector Machines. Automatic modeling systems. Structural parameter sensitivity. Constructive and pruning algorithms. Generalization capability optimization: cross-validation and Ockham's razor criterion based techniques. Min-Max neurofuzzy classifiers; standard and regularized training algorithm. ARC, PARC; Principal Component Analysis; Generalized Min-Max neurofuzzy networks. GPARC. Swarm Intelligence. Evolutionary Computation. Genetic algorithms. Particle Swarm Optimization, Ant Colony Optimization. Automatic feature selection. Fuzzy reasoning. Generalized modus ponens; FIS; fuzzyfication and e defuzzyfication. ANFIS. Basic and advanced training algorithms: clustering in the joint input-output space, hyperplane clustering. Outline of prediction and cross-prediction problems: embedding based on genetic algorithms. Applications and case studies: micro-grids energy flows modelling and control, Smart Grids optimization and control, classification of TCP/IP traffic flows. Mining of frequent patterns and rule extraction in large data bases (Big Data Analytics).