Educational guide 2022_23 School of Engineering

Contents

Topic	Sub-topic
Multidimensional data	Basic problems: prediction, classification, optimization, clustering, visualization. Data types: discrete, real, cathegorical. Data preprocessing: representation, outliers, missing data, scaling.
Neural computation	McCulloch & Pitts model: weights, thresholds, fields, activation function, activation. Artificial neural network architectures. Neuronal models classification.
Supervised learning	Linear model: multilinear regression. Simple perceptron. Linear separability. Cross-validation. Multilayer networks. Back-propagation. Variants of Back-propagation. Support Vector Machines. Other algorithms.
Unsupervised learning	Linear model: principal component analysis. Self-supervised networks. Hebbian learning. Competitive learning. Self-Organized Maps. Other algorithms.
Evolutionary computation	Genetic algorithms: chromosome, population, reproduction, recombination, mutation, fitness. Genetic programing. Particle Swarm Optimization. Other algorithms.