| Educational guide School of Engineering |
english |
| Computer Security Engineering and Artificial Intelligence (2016) - Online |
 Subjects |
| NEURONAL AND EVOLUTIONARY COMPUTING |
 
|
||||||||||||||||||
| IDENTIFYING DATA | 2018_19 | |||||||||||||||||
| Subject (*) | NEURONAL AND EVOLUTIONARY COMPUTING | Code | 17685106 | |||||||||||||||
| Study programme |
|
Cycle | 2nd | |||||||||||||||
| Descriptors | Credits | Type | Year | Period | Exam timetables and dates | |||||||||||||
| 4.5 | Compulsory | First | 1Q |
|||||||||||||||
| Modality and teaching language | See working groups | |||||||||||||||||
| Prerequisites | ||||||||||||||||||
| Department | Computer Engineering and Mathematics |
|||||||||||||||||
| Coordinator |
|
|||||||||||||||||
| Lecturers | |
|||||||||||||||||
| Web | ||||||||||||||||||
| General description and relevant information | Artificial neural networks and genetic algorithms constitute a wide and diverse set of models and techniques for data analysis, inspired by their biological equivalents: the nervous system and genetic evolution. In this course we will show the main models of neural and evolutionay computation, with emphasys on their capacity and use to solve problems of prediction, classification, optimization, clustering and visualization of multidimensional data. | |||||||||||||||||
| ||||||||||||||||||
| (*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation. | ||||||||||||||||||
