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.
Computational Science and Engineering
Oceanography
Objective: providing a basic knowledge of the physical oceanography and how to integrate theoretical knowledge with experimental measurements.
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.
Numerical Analysis
Objective: providing numerical analysis tools for scientific computing, with particular attention to linear algebra, polynomial approximation, numerical integration, numerical solution of ordinary differential equations and partial differential equations, approximation of eigenvalues and eigenvectors.
Computational Fluid Mechanics
Objective: introducing to the use of computational techniques to solve problems in fluid mechanics.
Information Theory
The course introduces the main concepts in information theory and algorithmic information theory.
Data Management for Big Data
Objective: introducing students to computational management of data, in particular the characterization of an information system, data modeling, design and management of databases, including non-traditional ones (eg, unstructured documents, spatial data, biological data , multimedia data), to the fundamentals of distributed data and to methodologies and techniques for the management and analysis of big data.
Advanced Numerical Analysis
Objective: introducing the student to state of the art methods for the numerical simulation of partial differential equation.
Introduction to Quantum Mechanics and Quantum Computing
Introducing to main concepts in quantum mechanics and quantum computing
Statistical Methods for Data Science
Objective: presenting the basic elements and principles of inferential statistics and statistical techniques for the analysis of complex data.
Computational Physics Laboratory
Objective: providing fundamental tools and numerical algorithms for solving problems of classical physics and simple problems of quantum physics.
Introduction to Quantum Information Theory
The course introduces the main concepts and effects of quantum correlation on information theory, computation and machine learning.
Software Development Methods
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.
Molecular Simulation
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.
Radiative Processes in Astrophysics
Introduce the student to the physics of the inner stars and on radiative processes fundamental in astrophysics.
Computer Vision and Pattern Recognition
Objective: introducing machine learning techniques for artificial vision and pattern recognition in sequential data.
Numerical Methods in Quantum Mechanics
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.
Advanced Programming and Algorithmic Design
Objective: providing advanced knowledge of both theoretical and practical programming in C / C ++ and Python, with particular regard to the principles of object oriented programming and best practices of software development (advanced use of version control systems, continuous integration, unit testing), and introducing the modern technology of algorithms development.
Simulation of Multibody Systems
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.
Stochastic Modelling and Simulation
Objective: introducing students to the fundamentals and practice of stochastic modeling, simulation of stochastic models and inference of parameters starting from observations, with a focus on scalability for large models.
Computational Quantum Chemistry
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.
Optimisation 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.
Statistical Mechanics
Objective: providing methods and results of the elementary statistics mechanics in equilibrium.
Probabilistic Machine Learning
Objective: presenting advanced machine learning techniques, with a focus on Bayesian methods.
Cyber-Physical Systems
Objective: introducing cyber-physical systems, with particular regard to modeling them with hybrid formalisms and the formal verification of their properties.
Galaxy Astrophysics
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=...
Fluid Dynamics
Objective: introducing the physical and mathematical principles of fluid dynamics.
Formation of Cosmological Large-Scale Structures
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.
Physics and Modelling of Turbulent Flows
Objective: introducing to the dynamics of highly non-linear processes (turbulence) in fluid dynamics, and to the computational techniques used to solve such models.
Introduction to Cosmology
Objective: introducing to the foundations of modern cosmological theories.
Advanced Mathematical Methods
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.
Theoretical Astrophysics
Objective: providing the elements of the physics of the stellar interior and of important radiative processes in astrophysics.
Biofluidodynamics
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.
Dynamical systems
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.
Environmental Hydraulics
Objective: providing the basic knowledge of fluid dynamics at the environmental scale and of tools for numerical modeling.
Introduction to Machine Learning
Objective: Introduce the students to the machine learning fundamentals, to the main techniques on supervised learning, and to the principal application domains. Present evolutionary calculation. The course explains how to design, develop and evaluate simple ML-based end-to-end systems and, at the same time, how to describe their operations.
Foundations of High Performance Computing
Objective: introducing students to modern architectures for high performance computing. Students will learn how to properly test such architectures (computing power, bandwidth, latency, energy efficiency). Leveraging these skills, students will be introduced to the parallel programming based on MPI protocols (Message Passing Interface) and multi-threading with OpenMP.
Physics of Atmosphere
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.
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.