| Educational guide School of Engineering |
english |
| Computer Security Engineering and Artificial Intelligence (2016) |
 Subjects |
| NEURONAL AND EVOLUTIONARY COMPUTING |
| Contents |
| IDENTIFYING DATA | 2016_17 |
| 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: outliers, missing data, scaling. |
| Neuronal computation | McCulloch & Pitts model: weightes, threshols, fields, activation function, activation. Artificial neural network architectures. Neuronal models classification. |
| Associative memory and optimization | Hopfield networks: Hebb rule, dynamics, energy. Application for combinatiorial optimization. |
| Supervised learning | Linear model: multilinear regression. Simple perceptron. Linear networks. Linear separability. 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. Adaptive Resonance Theory. Other algorithms. |
| Evolutionary computation | Genetic algorithms: chromosome, population, reproduction, recombination, mutation, fitness. Genetic programing. Particle Swarm Optimization. Other algorithms. |
