COURSE OBJECTIVES

To covers modern methods related to artificial intelligence with a critical view of their application in medical research.

MAIN TOPICS 

• Introduction to machine learning – 2h Daniela Besozzi
• Unsupervised learning – 2h Giancarlo Mauri 
• Neural networks and deep learning – 4h Andrea Tangherloni
• Bayesian and causal networks – 2h Fabio Stella (in presence)
• Explainable artificial intelligence – 2h Marco S. Nobile (in presence)
• Validation of machine learning models for decision support in medicine – 2h Federico Cabitza (in presence)
• Theory vs. big data: complex systems biology for the right blend – 2h Alex Graduenzi (in presence)
• Deterministic, stochastic and hybrid agent-based simulation methods– 2h Marco Antoniotti  (in presence)
• COVID-19: from biological/clinical data to epidemiological models – 3h Daniela Besozzi (in presence)
• Big data and bioinformatics in biomedical research – 3h Maurizio Callari (in presence)
• Mutational signatures in human cancers – 2h Daniele Ramazzotti (in presence)
• Integrative clustering of genomic data – 2h Daniele Ramazzotti (in presence)
• Computational methods for big medical-imaging data – 2h Isabella Castiglioni (in presence)
• Radiomics in oncological and fibrous diseases imaging – 2h Elisabetta De Bernardi (in presence)