Information Theory
The course introduces the main concepts in information theory and algorithmic information theory.
Courses
The program in Data Science and Scientific Computing is organized in two curricula:
Each curriculum is further organized in several study plans, each corresponding to a specific application area, proposing a choice of specialization courses.
Check out the Study Plan page for more information.
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.
Advanced Numerical Analysis
Objective: introducing the student to state of the art methods for the numerical simulation of partial differential equation.
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.
Algorithms for Massive Data
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.
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.
Bayesian Statistics
Objective: introducing techniques of analysis and statistical Bayesian inference.
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.
Bioinformatics
Objective: introduce 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 the latest generation sequencing technologies.
Computational and Systems Neuroscience
The course aims at introducing the student to modeling and analysis techniques, also data-based, in the area of computational neusciences.
Computational Methods for Turbulent Fluids
Objective: introducing to the dynamics of highly non-linear processes (turbulence) in fluid dynamics, and to the computational techniques used to solve such models.
Computational Physics Laboratory
Objective: providing fundamental tools and numerical algorithms for solving problems of classical physics and simple problems of quantum physics.
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.
Computer Vision and Pattern Recognition
Objective: introducing machine learning techniques for artificial vision and pattern recognition in sequential data.
Notes: Begins on Octorber 9th.
Control Theory
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.
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.
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.
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.
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.
Earth Sciences Analytics
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.
Energy and Georesources Analytics
The course will introduce students to main ideas, problems, data sources and models in georesource identification and management and in energy production.
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.
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.
Genomic Data Analytics
Objective: presenting statistical methods and computational analysis techniques in genomics.
Geophysics Analytics
The course will introduce the student to the main problems, data sources, and models in Geophysics.
Health Data Analytics
Objective: introducing advanced computational and statistical techniques for the analysis of clinical data.
Information Retrieval and Data Visualization
Objective: presenting the most important conceptual aspects of information retrieval systems and both principles and techniques for data visualization.
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.
Introduction to Quantum Information Theory
The course introduces the main concepts and effects of quantum correlation on information theory, computation and machine learning.
Introduction to Quantum Mechanics and Quantum Computing
Introducing to main concepts in quantum mechanics and quantum computing
Management of Health Data
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.
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.
Natural Language Processing
Objective: introduce modern computational techniques and machine learning techniques for the analysis of natural language.
Network Science
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.
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.
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.
Oceanography and Marine Ecology Analytics
The course will introduce the student to the main ideas, problems, models and data analytic methods in oceanography and in marine ecology.
Open Data Management and the Cloud
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.
Optimisation and Design
Objective: providing expertise on optimization techniques, with applications to industrial design.
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.
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.
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.
Probabilistic Machine Learning
Objective: presenting advanced machine learning techniques, with a focus on Bayesian methods.
Radiative Processes in Astrophysics
Introduce the student to the physics of the inner stars and on radiative processes fundamental in astrophysics.
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.
Social Network Analysis
Objective: presenting statistical analysis techniques for social networks and other social and economic networks.
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.
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.
Statistical Mechanics
Objective: providing methods and results of the elementary statistics mechanics in equilibrium.
Statistical Methods for Data Science
Objective: presenting the basic elements and principles of inferential statistics and statistical techniques for the analysis of complex data.
Detailed description (a.a. 2017/18): See here.
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.