| Educational guide School of Engineering |
english |
| Biomedical Data Science (2022) |
 Subjects |
| DEEP LEARNING |
 
|
||||||||||||||||||
| IDENTIFYING DATA | 2023_24 | |||||||||||||||||
| Subject (*) | DEEP LEARNING | Code | 17705115 | |||||||||||||||
| Study programme |
|
Cycle | 2nd | |||||||||||||||
| Descriptors | Credits | Type | Year | Period | Exam timetables and dates | |||||||||||||
| 4.5 | Compulsory | Second | 1Q |
|||||||||||||||
| Modality and teaching language | See working groups | |||||||||||||||||
| Prerequisites | ||||||||||||||||||
| Department | Electronic, Electric and Automatic Engineering Computer Engineering and Mathematics |
|||||||||||||||||
| Coordinator |
|
antoni.monleon@urv.cat roser.sala@urv.cat petiaivanova.radeva@urv.cat emilynatasha.diaz@urv.cat bhalaji.nagarajan@urv.cat |
||||||||||||||||
| Lecturers |
|
|||||||||||||||||
| Web | ||||||||||||||||||
| General description and relevant information |
The main goal of this subject is to introduce the Deep learning as a subset of machine learning, in terms of Neural networks. We will show how these neural networks attempt to simulate the behaviour of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimise and refine for accuracy. We will illustrate Deep learning driving many artificial intelligence applications and services in healthcare that improve automation, performing analytical tasks without human intervention. We will show how Neural networks are able to solve problems as medical image retrieval, classification, segmentation and analysis, and how these tasks can help health professionals to increase the quality of the patient care. This course is coordinated by Universitat de Barcelona (Dept. Mathematics and Computer Science). |
|||||||||||||||||
| ||||||||||||||||||
| (*)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. | ||||||||||||||||||
