Machine Learning and Data Analytics
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.
Foundations of Data Science
Statistical Machine Learning
Objective: presenting advanced machine learning techniques, with a focus on deep learning and Bayesian methods.
Detailed description (a.a. 2017/18): See here.
Big Data Bioinformatics
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.
Cyber-Physical Systems
Objective: introducing cyber-physical systems, with particular regard to modeling them with hybrid formalisms and the formal verification of their properties.
Optimization Models
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.
Data Analytics and Statistical Learning
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.
Cyber-Physical Systems and Reinforcement Learning
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.