This advanced Master-level course explores modern machine and deep learning techniques tailored for biological signal processing and human data analysis. Moving beyond static text and images, the curriculum emphasizes dynamic, spatio-temporal signals that exhibit complex correlations across both spatial dimensions (e.g., sensor arrays) and temporal domains (e.g., physiological time series).
The course places a strong, distinct focus on unsupervised learning and anomaly detection, a critical and yet often neglected skill sets, necessary to analyze unannotated biomedical data. Students will gain theoretical mastery and practical expertise across unsupervised feature extraction (SOMs, DBSCAN, autoencoders, one class support vector machines), supervised deep architectures (CNNs, RNNs, GNNs, Transformers, SNNs), and advanced model optimization.
Hands-on laboratory sessions with tested reference code prepare students to build custom end-to-end models. Assessment is project-based (groups of max 2), requiring students to analyze real-world datasets, deliver a scientific-style report, submit working code, and complete an oral defense.
The course places a strong, distinct focus on unsupervised learning and anomaly detection, a critical and yet often neglected skill sets, necessary to analyze unannotated biomedical data. Students will gain theoretical mastery and practical expertise across unsupervised feature extraction (SOMs, DBSCAN, autoencoders, one class support vector machines), supervised deep architectures (CNNs, RNNs, GNNs, Transformers, SNNs), and advanced model optimization.
Hands-on laboratory sessions with tested reference code prepare students to build custom end-to-end models. Assessment is project-based (groups of max 2), requiring students to analyze real-world datasets, deliver a scientific-style report, submit working code, and complete an oral defense.
- Docente: Michele Rossi
The course provides an introduction to human pathophysiology and to the major molecular mechanisms underlying rare, multifactorial, and complex diseases, including cancer, neurodevelopmental disorders, and age-related diseases.
The course also introduces the main technologies currently available for the large-scale and multidimensional investigation of biological systems, with particular emphasis on omics technologies and the data they generate in both clinical and non-clinical settings. Approaches for the analysis, integration, and interpretation of omics data are presented in the context of human disease research.
Students will acquire bioinformatics skills for the analysis and interpretation of omics data and will learn how to use these data to formulate biologically meaningful hypotheses about the molecular mechanisms underlying complex diseases. Particular attention will also be given to the interpretation of genetic variants and their potential contribution to disease.
The course also introduces the main technologies currently available for the large-scale and multidimensional investigation of biological systems, with particular emphasis on omics technologies and the data they generate in both clinical and non-clinical settings. Approaches for the analysis, integration, and interpretation of omics data are presented in the context of human disease research.
Students will acquire bioinformatics skills for the analysis and interpretation of omics data and will learn how to use these data to formulate biologically meaningful hypotheses about the molecular mechanisms underlying complex diseases. Particular attention will also be given to the interpretation of genetic variants and their potential contribution to disease.
- Docente: Emanuela Leonardi