Learning objectives

The course intends to provide the main theoretical and application concepts of some advanced machine learning techniques for the analysis and management of biological data, with particular emphasis on biomedical image. The problems and techniques related to the development of unsupervised Pattern Recognition systems will be presented and discussed, such as clustering, anomaly detection and similar. The main problems related to the processing and analysis of clinical studies based on diagnostic imaging will also be addressed, as well as the fundamental algorithms for resolving such situations such as image segmentation, registration and compression. After the course, students will be able to analyze a large class of unsupervised biological problems, using the Pattern Recognition point of view, and will have the necessary knowledge to design, develop and manage the different components of a pattern recognition system in real-world biomedical data contexts. They will also be able to use the image processing algorithms seen in class to solve typical problems that arise in clinical studies based on diagnostic imaging, by applying state-of-the-art methodologies and software.