COURSE OBJECTIVES
Introduction to Machine Learning and Big Data
To cover modern methods related to artificial intelligence.

Big Data and Real World Studies – Winter School
To understand the different types of studies in epidemiological research and applications of bioinformatics on big data in medical research.

TOPICS
Introduction to Machine Learning and Big Data
Introduction to machine learning – 2h Daniela Besozzi
Unsupervised learning – 2h Daniela Besozzi
Neural networks and deep learning – 4h Daniele Papetti

Big Data and Real World Studies – Winter School
Epidemiological designs – 7h Paola Rebora
Epidemiological designs – 3h Anita Andreano
Introduction to Real World Data, Geo-spatial analysis – 4h Marco Villa
Real world study – 2h Carlo Alberto Scirè
Introduction to causal models – 4h Davide Bernasconi
Computational methods for big medical-imaging data – 2h Isabella Castiglioni
Radiomics in oncological and fibrous diseases imaging – 2h Elisabetta De Bernardi
From data to models in pandemics – 2h Daniela Besozzi
Fairies in the modern world: applications and impact of artificial intelligence in cardiac imaging – 2h Camilla Torlasto
Mutational signatures in human cancers – 2h Daniele Ramazzotti
Integrative clustering of genomic data – 2h Daniele Ramazzotti
Interpretable and explainable artificial intelligence – 2h Marco S. Nobile
Computational intelligence for biomedicine and healthcare – 2h Marco S. Nobile
Bayesian and causal networks - part I – 2h Fabio Stella
Bayesian and causal networks - part II – 2h Fabio Stella