| Educational guide School of Engineering |
english |
| Computer Security Engineering and Artificial Intelligence (2016) - Online |
 Subjects |
| NEURONAL AND EVOLUTIONARY COMPUTING |
| Learning outcomes |
| IDENTIFYING DATA | 2019_20 |
| 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 |
| Type A | Code | Learning outcomes |
| A1 |
Analyse the problems and their causes from a global focus in the medium and long term. | |
| A7 |
Understand the difficulty in handling real multidimensional data, and know some classical linear techniques. Know neuronal and evolutionary computing techniques applicable to problems regarding the prediction, classification, optimisation, grouping and display of multidimensional data. | |
| G2 |
Apply the techniques learned in a specific context. | |
| Type B | Code | Learning outcomes |
| CT1 |
Manage and communicate complex information in foreign language. | |
| CT3 |
Recognise the situation as a problem in a multidisciplinary, research or professional environment, and take an active part in finding a solution. | |
| CT5 |
Produce a persuasive, consistent and precise discourse that can explain complex ideas and effectively interact with the audience. | |
| Type C | Code | Learning outcomes |
