Machine Learning for Network and Genomic Medicine

The main aim of the course is to introduce and discuss some of the state-of-the-art Machine Learning (ML) methods for the analysis of complex biological systems, such as networks of proteins, genes and drugs modeled as graphs and processed through semi-supervised learning methods for node label and edge prediction problems.
Deep Neural Networks and ensembles of learning machines are introduced as well, as cutting edge ML approaches for relevant problems in Genomic Medicine, such as the prediction of deleterious and pathogenic variants associated with genetic diseases, and the differential molecular diagnosis of rare diseases.
A background in Machine Learning is welcome but not mandatory.
The course is conceived for Computer Science students, but students in Mathematics, Physics, Chemistry, Life Sciences, Pharmacology and Medicine are welcome.