Objective: introduce the student to the principles of learning from data based on statistics, and to the scientific treatment of data to obtain new and reproducible knowledge. Some of the main supervised and unsupervised statistical learning techniques are presented.
Objective: introducing cyber-physical systems, with particular regard to modeling them with hybrid formalisms and the formal verification of their properties. Presenting both notions and main techniques of Reinforcement Learning.
Objective: introducing students to the principles of data analysis, to data mining, and to machine learning (supervised and unsupervised learning).
Detailed description (a.a. 2017/18): See here.
Objective: presenting advanced machine learning techniques, with a focus on deep learning and Bayesian methods.
Detailed description (a.a. 2017/18): See here.
Objective: introducing the main algorithmic methods for the storage, compression and analysis of large amounts of biological data, with particular emphasis on the treatment of sequencing data produced with next generation sequencing technologies.
Objective: introducing cyber-physical systems, with particular regard to modeling them with hybrid formalisms and the formal verification of their properties.
Objective: providing students with the methodological, theoretical and practical tools to formulate linear programming models and combinatorial optimization problems and to solve them, even for high dimensionality problems, using appropriate optimization software.