| Educational guide School of Engineering |
english |
| Computer Security Engineering and Artificial Intelligence (2016) - Online |
 Subjects |
| NEURONAL AND EVOLUTIONARY COMPUTING |
| Learning outcomes |
| IDENTIFYING DATA | 2022_23 |
| 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 |
| CT3 |
Recognise the situation as a problem in a multidisciplinary, research or professional environment, and take an active part in finding a solution. Follow a systematic method with an overall approach to divide a complex problem into parts and identify the causes by applying scientific and professional knowledge. Design a new solution by using all the resources necessary and available to cope with the problem. Draw up a realistic model that specifies all the aspects of the solution proposed. Assess the model proposed by contrasting it with the real context of application, find shortcomings and suggest improvements. | |
| Type C | Code | Learning outcomes |
