Educational guide School of Engineering |
english |
Computer Security Engineering and Artificial Intelligence (2016) - Online |
Subjects |
NEURONAL AND EVOLUTIONARY COMPUTING |
Contents |
IDENTIFYING DATA | 2021_22 |
Subject | NEURONAL AND EVOLUTIONARY COMPUTING | Code | 17685106 | |||||
Study programme |
|
Cycle | 2nd | |||||
Descriptors | Credits | Type | Year | Period | ||||
4.5 | Compulsory | First | 1Q |
Competences | Learning outcomes | Contents |
Planning | Methodologies | Personalized attention |
Assessment | Sources of information | Recommendations |
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: weightes, threshols, fields, activation function, activation. Artificial neural network architectures. Neuronal models classification. |
Associative memory and optimization | Associative memory and optimization Hopfield networks: Hebb rule, dynamics, energy. Application for combinatiorial optimization. |
Supervised learning | Linear model: multilinear regression. Simple perceptron. Linear separability. Cross-validation. Multilayer networks. Back-propagation. Variants of Back-propagation. Cascade Correlation. 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. |