2nd year

The course introduces the main concepts and effects of quantum correlation on  information theory, computation and machine learning.

Objective: introducing to the dynamics of highly non-linear processes (turbulence) in fluid dynamics, and to the computational techniques used  to solve such models.

The course aims at introducing the student to modeling and analysis techniques, also data-based, in the area of computational neusciences.

Objective: providing advanced notions of the theory of dynamical systems both in continuous and in discrete time. Introduce to modern techniques for the design of complex control systems with particular reference to application contexts of engineering interest in the industrial field.

Objective: introducing advanced computational and statistical techniques for the analysis of clinical data.

Objective: providing the foundations of the modern approach to the control of dynamical systems, with particular reference to the treatment of uncertainty, structured and unstructured. Provide the main tools and methods for the analysis and synthesis of multiple-input-multiple-output control systems.

Objective: introducing concepts and techniques for collaborative development of large and complex software systems for industrial applications, including Java, software development lifecycle, best practices in software development as code testing, versioning, and design patterns.

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.

Objective: providing expertise for the management of clinical and biomedical data from computerized medical records, through the methods of health information technology and of process modeling.

Objective: providing students with practical information on how to design data models and data structures, to manage metadata to optimize access and research, and to become familiar with interoperability standards. The course will focus on the concept of open data, with efficiency for big data projects, and the concept of cloud as an infrastructure for data management and their processes.

Objective: provide tools for the analysis of images and signals in the biomedical field.

Objective: presenting the most important conceptual aspects of information retrieval systems and both principles and techniques for data visualization. 

Objective: introducing machine learning techniques for artificial vision and pattern recognition in sequential data.

Genome sequencing technologies have revolutionised our approach to study biological systems. From healthy tissues to diseases, many questions are approached through the collection of far larger sequencing datasets, and some argue that all modern biology is computational biology. Sequencing technologies can measure molecular states at various degrees of resolution and noise, and complex bioinformatics/ machine learning pipelines are required to extract complex patterns that characterise from basic cellular processes to disease progression.

Objective: presenting statistical methods and computational analysis techniques in genomics.

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 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: introduce modern computational techniques and machine learning techniques for the analysis of natural language.

Objective: introducing to the main techniques for the design of algorithms and data structures to manipulate strings, trees and large graphs, in particular to compression techniques and randomization.

NOTE: The equivalent name of this course is "Algorithms for Massive Data".

The course will introduce the student to the main problems, data sources, and models in Geophysics.

Objective: providing expertise on optimization techniques, with applications to industrial design.

The course will introduce the student to data sources, problems, main concepts and models in Earth Science, with a focus on analytics using these data. 

Objective: You will learn how to organize, transform, analyse and visualize data, with a focus on the relational data model, and a detour to semistructured data. You will learn the fundamentals of data science using R environment.

The course will introduce the student to the main ideas, problems, models and data analytic methods in oceanography and in marine ecology.

Objective: introducing techniques of analysis and statistical Bayesian inference.

The course will introduce students to main ideas, problems, data sources and models in georesource identification and management and in energy production.

Objective: presenting statistical analysis techniques for social networks and other social and economic networks.

NOTE: The equivalent name of this course is "Social Network Analysis".

The course introduces the main concepts in information theory and algorithmic information theory.

Objective: introducing cyber-physical systems, with particular regard to modeling them with hybrid formalisms and the formal verification of their properties.

Introducing to main concepts in quantum mechanics and quantum computing