Objective: introducing the physical and mathematical principles of fluid dynamics.
Objective: introducing to the foundations of modern cosmological theories.
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.
Objective: providing the elements of the physics of the stellar interior and of important radiative processes in astrophysics.
The course aims at introducing the student to modeling and analysis techniques, also data-based, in the area of computational neusciences.
Objectives: training students from different disciplines, such as applied mathematics, physics, engineering, to integrate theory and models in the study of some problems arising in applied sciences and which result in partial differential equation. Provide students with a mathematical background suitable to analyze them.
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.
Introduce the student to the physics of the inner stars and on radiative processes fundamental in astrophysics.
Objective: introducing advanced computational and statistical techniques for the analysis of clinical data.
Objective: providing expertise on the motion of fluids inside the human body, especially in the cardiovascular system, with focus on the evaluation in a clinical settings.
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.
The main ideas and methodologies of Reinforcement Learning will be introduced.
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 the basic knowledge of fluid dynamics at the environmental scale and of tools for numerical modeling.
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: introduces the students to the design and analysis of Cyber-Physical Systems, we will see how to model such systems, how to specify and monitor their behaviors using formal languages as temporal logics, and how to use monitoring techniques for different applications as parameter synthesis and falsification test
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: provide knowledge of the fundamental properties of the dynamics and thermodynamics of the atmosphere, and the formulation and implementation of some simple analytical models of atmospheric dynamical systems.
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: providing a basic knowledge of the physical oceanography and how to integrate theoretical knowledge with experimental measurements.
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.
Objective: introducing to the use of computational techniques to solve problems in fluid mechanics.
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: providing fundamental tools and numerical algorithms for solving problems of classical physics and simple problems of quantum physics.
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: introducing the computational techniques used in molecular modeling and simulation, and illustrating how these techniques can be employed to describe and/ or predict chemical, physical and biological phenomena.
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".
Objective: providing an introduction to numerical methods and techniques for the numerical solution of quantum mechanical problems, especially in atomic physics and condensed matter, with a practical approach.
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.
Objective: providing the ability to understand the functioning and the internal structure of a molecular dynamics program. Being able to write code for molecular dynamics simulations and to analyze the output.
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.
This course will be available from the Academic Year 2022/23.
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.
Objective: providing knowledge of the most important theoretical formalism used in quantum chemistry, and of the main computational methods, numerical algorithms, and software tools in the field of quantum chemistry.
The course will introduce the student to the main ideas, problems, models and data analytic methods in oceanography and in marine ecology.
This course will be available from the Academic Year 2022/23.
Objective: introducing techniques of analysis and statistical Bayesian inference.
Objective: providing methods and results of the elementary statistics mechanics in equilibrium.
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".
Objective: providing an overview, in the context of modern astronomy, to the various cosmic objects and give the basic principles necessary for the determination of their fundamental physical quantities.
Esse3 Course Page A.Y 2019/2020: https://esse3.units.it/Guide/PaginaADErogata.do?cod_lingua=eng&ad_er_id=...
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.
Objective: providing a thorough and updated knowledge of cosmology issues related to the study of the formation of galaxies, clusters of galaxies and the structure of large-scale Universe in current cosmological models, through analytical, numerical, and statistical techniques for the evolution of disturbances in linear and nonlinear regime.
Introducing to main concepts in quantum mechanics and quantum computing