This course will be organized as series of reading groups or specialized seminars by members or collaborators of the research unit on Natural Networks (Networks).

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Advanced Topics of Complex Networks

Corpo:

This course will be organized as series of reading groups or specialized seminars by members or collaborators of the research unit on Natural Networks (Networks).

Ore:

20

Professors:

Guido Caldarelli (IMT Lucca), Walter Quattrociocchi (IMT Lucca)

Compulsory:

Advanced Topics of Computer Science

Corpo:

This course will be organized as series of reading groups or specialized seminars by members or collaborators of the research unit on System Modelling and Analisys (SysMA).

Ore:

40

Professors:

Rocco De Nicola (IMT Lucca), Mirco Tribastone (IMT Lucca), Stefania Gnesi (CNR), Marinella Petrocchi (CNR)

Disponibile:

Advanced Topics of Image Analysis

Corpo:

This course will be organized as series of reading groups or specialized seminars by members or collaborators of the research unit on Pattern Recognition and Image Analysis (PRiAn).

Ore:

20

Professors:

Sotirios Tsaftaris (The University of Edinburgh)

Compulsory:

Disponibile:

Algorithmics

Corpo:

This course covers basic and advanced foundations, problems and solutions of algorithmic computation. A first part offer an overview of the fundamental notions of algorithm analysis and recalls algorithmic solutions (and their complexity) for some basic problems like sorting and searching. The second part of the course will focus on advanced algorithms which are essential in some of the research fields relevant to the different curriculum of the Computer Decision and System Science track.

Ore:

20

Professors:

Walter Quattrociocchi (IMT Lucca)

Disponibile:

Artificial Intelligence

Corpo:

TBD

Ore:

20

Professors:

Marco Gori (Università degli Studi di Siena)

Disponibile:

Basic Numerical Linear Algebra

Corpo:

The course is aimed to introduce the basic notions about vector spaces, vectors, matrices, and norms, along with the basic numerical methods concerning the solution linear systems. In particular: direct methods for square linear systems and conditioning analysis; direct methods for solving over-determined linear systems in the least square sense, with applications. The course also provides an introduction to Matlab, which is used for implementing the methods.

Ore:

20

Professors:

Luigi Brugnano (Università degli Studi di Firenze)

Compulsory:

Disponibile:

Computer Programming and Methodology

Corpo:

This course aims at introducing to students principles and methodologies of computer programming. Emphasis is on good programming style, techniques and tools that allow efficient design, development and maintenance of software systems. The course focuses on the design of computer applications drawing attention to modern software engineering principles and programming techniques, like object-oriented design, decomposition, encapsulation, abstraction, and testing. A significative case study is used to allow students to experiment with the principles and techniques considered in this course. Depending on the background of the class, Java, C++, and/or Python are considered in the course.

Ore:

20

Professors:

Michele Loreti (Università degli Studi di Firenze)

Compulsory:

Disponibile:

Convex Optimization

Corpo:

The course aims at giving a modern and thorough treatment of algorithms for solving convex, large-scale and nonsmooth optimization problems. Applications of convex optimization. Convex sets, functions and optimization problems. Optimality conditions. Basic algorithms for unconstrained optimization (gradient, fast gradient and Newton methods). Basic algorithms for constrained optimization (Interior point and active set methods). Subdifferential and conjugate of convex functions. Duality. Proximal mappings. Proximal minimization algorithm. Augmented Lagrangian Method. Forward-Backward and Douglas-Rachford splitting. Alternating Direction Method of Multipliers (ADMM). Coordinate descent.

Ore:

20

Professors:

Stephen Boyd (Stanford University)

Compulsory:

Disponibile:

Data Science with Complex Networks

Corpo:

Complex Systems are everywhere and in the era of massive production of electronic data coming from all sort of devices it is of crucial importance to have the right tools to manage and extract from them all the valuable information. To this aim during this course we will develop both the basic theoretical tools and the practical coding technics to tackle all sort of complex systems, ranging from Trade and Financial Networks, to the World Wide Web and the Social Networks. In particular Complex Networks Theory proved to be successful in the process of handling this enormous quantity of data and in order to apply these concepts to the various cases it is crucial to define a clear strategy and guidelines to represent the system data in the shape of a network. Using the Python scripting language we will introduce state of the art methods and algorithms to cope with some reference dataset.

Ore:

20

Professors:

Guido Caldarelli (IMT Lucca), Alessandro Chessa (IMT Lucca)

Compulsory:

Ethics and Research: Objectivity, Neutrality and Values in Science

Corpo:

The course has been cancelled

Ore:

10

Professors:

Tbd

Foundations of Probability Theory and Statistical Inference

Corpo:

This course aims at introducing the fundamental concepts of probability theory and statistical

inference.

Some proofs are sketched or omitted in order to have more time for examples, applications and

exercises.

In particular, the course deals with the following topics:

? probability space, random variable, expectation, variance, cumulative distribution function, discrete and absolutely continuous distributions, random vector, joint and marginal distributions, joint cumulative distribution function, covariance,

? conditional probability, independent events, independent random variables, conditional probability density function, order statistics,

? multivariate Gaussian distribution,

? probability-generating function, Fourier transform/characteristic function,

? types of convergence and some related important results,

? point estimation, interval estimation, hypothesis testing, linear regression, introduction to Bayesian statistics.

inference.

Some proofs are sketched or omitted in order to have more time for examples, applications and

exercises.

In particular, the course deals with the following topics:

? probability space, random variable, expectation, variance, cumulative distribution function, discrete and absolutely continuous distributions, random vector, joint and marginal distributions, joint cumulative distribution function, covariance,

? conditional probability, independent events, independent random variables, conditional probability density function, order statistics,

? multivariate Gaussian distribution,

? probability-generating function, Fourier transform/characteristic function,

? types of convergence and some related important results,

? point estimation, interval estimation, hypothesis testing, linear regression, introduction to Bayesian statistics.

Ore:

30

Professors:

Irene Crimaldi (IMT Lucca)

Compulsory:

Disponibile:

Funding and Management of Research and Intellectual Property (long seminar without exam)

Corpo:

This long seminar aims at providing an overview on the management of intellectual property rights (copyright transfer agreements; open access; patents, etc.). Funding opportunities for PhD students, post-docs, and researchers are also presented (scholarships by the Alexander von Humboldt Foundation; initiatives by the Deutscher Akademischer Austausch Dienst; scholarships offered by the Royal Society in UK; bilateral Italy-France exchange programmes; Fulbright scholarships; Marie Curie actions; grants for researchers provided by the European Research Council). For each funding scheme, specific hints on how to write the proposal are given.

Ore:

10

Professors:

Marco Paggi (IMT Lucca)

Introduction to Network Theory

Corpo:

TBD

Ore:

10

Professors:

Guido Caldarelli (IMT Lucca)

Disponibile:

Large Scale Image Analysis for Natural and Life Sciences

Corpo:

Principles of imaging modalities (optical microscopy, spectroscopy, CT, MRI, PET, SPECT) and their applications in natural and life sciences (Dharmakumar); Basics of image analysis (filtering, segmentation, detection) and basics of statistical mining; Designing robust image analysis methods; Large-scale analysis; Integration with databases and knowledge sharing platforms; Error testing and precision bound repetition studies for longitudonal and group studies (phenotyping); High performance computing for imaging (computer vision); Scientific and data visualization; Prerequisites: Probability and basic random processes, basic computer programming, statistics (or econometrics), databases.

Ore:

20

Professors:

Sotirios Tsaftaris (The University of Edinburgh), Rohan Dharmakumar (Cedars-Sinai Medical Center)

Compulsory:

Disponibile:

Machine Learning and Pattern Recognition

Corpo:

Basics of pattern recognition and machine learning and real world applications in imaging, internet, finance. Similarities and differences. Decision theory, ROC curves, Likelihood tests. Linear and quadratic discriminants. Template based recognition and feature detection/extraction. Supervised learning (Support vector machines, Logistic regression, Bayesian). Unsupervised learning (clustering methods, EM, PCA, ICA). Current trends in Machine Learning. Prerequisites: Probability and basic random processes, linear algebra, basic computer programming, numerical methods.

Ore:

20

Professors:

Sotirios Tsaftaris (The University of Edinburgh)

Compulsory:

Disponibile:

Model Checking

Corpo:

Model checking is an automated formal verification technique whose main idea is to formally specify both the system specification and its properties (typically, by means of temporal logic) and automatically verify that such properties are satisfied (or to which extent they are). This course aims at presenting the fundamentals of model checking techniques for the verification of distributed and concurrent systems. Different classes of temporal logics will be introduced that rely on the use of semantic models to provide a logical framework for the analysis and verification of complex systems. The first part of the course will cover the fundamentals of qualitative model checking, while the second part of the course will cover the fundamentals of probabilistic model checking and its application to performance evaluation.

Ore:

20

Professors:

Alberto Lluch Lafuente (Technical University of Denmark), Mirco Tribastone (IMT Lucca)

Disponibile:

Model Predictive Control

Corpo:

Quick review of linear dynamical systems in state-space form, stability, state-feedback control and observer design, linear quadratic regulation and Kalman filtering. Basic model predictive control (MPC) algorithm and the receding horizon principle. Linear MPC: formulation, quadratic programming, stability properties. Multiparametric programming and explicit MPC. MPC of hybrid dynamical systems subject to linear and logical constraints. Stochastic MPC. Selected applications of MPC to automotive and aerospace systems, supply chains, financial engineering. Prerequisites: Linear algebra and matrix computation, calculus and mathematical analysis, optimization.

Ore:

20

Professors:

Alberto Bemporad (IMT Lucca)

Compulsory:

Disponibile:

Modelling and Verification of Reactive Systems

Corpo:

Computing systems are becoming increasingly sophisticated and control key aspects of our lives. In light of the increasing complexity of such computing devices, one of the key scientific challenges in computer science is to design and develop computing systems that do what they were expected to do, and do so reliably. The aim of this course is to introduce models for the formal description of computing systems, with emphasis on parallel, reactive and possibly real-time systems, and the techniques for system verification and validation that accompany them. As an important component of the course, we shall introduce industrial-strength software tools for modelling and analyzing the behaviour of (real-time) reactive systems.

Ore:

20

Professors:

Rocco De Nicola (IMT Lucca)

Disponibile:

Network Theory

Corpo:

Course description: Basic of Graph Theory: degree, clustering, connectivity, assortativity, communities. Analysis of Complex Networks, datasets and software. Community Detection, Modularity, Spectral Properties. Fractals, Self-Organised Criticality, Scale Invariance. Random Graph, Barabasi Albert Model, Fitness model, Small world. HITS Algorithm and PageRank. Real instances of Complex Networks in Biology and Social Sciences. Board of Directors, Ownership Networks, measures of Centrality and Control. World Trade Web, Minimal Spanning Trees, Competition and Products spaces. Prerequisites: Linear algebra and matrix computation, calculus and mathematical analysis.

Ore:

10

Professors:

Guido Caldarelli (IMT Lucca), Antonio Scala (CNR - Istituto di Sistemi Complessi)

Compulsory:

Optimal Control

Corpo:

Discrete-time optimal control: dynamic programming for finite/infinite horizon and deterministic/stochastic optimization problems. LQ and LQG problems, Riccati equations, Kalman filter. Deterministic continuous-time optimal control: the Hamilton-Jacobi-Bellman equation and the Pontryagin?s principle. Examples of optimal control problems in economics.

Ore:

20

Professors:

Giorgio Stefano Gnecco (IMT Lucca)

Compulsory:

Disponibile:

Principles of Concurrent and Distributed Programming

Corpo:

The course objective is to introduce the basics of concurrent programming problems through an illustration of the concepts and techniques related to modeling systems in which there are more components that are simultaneously active and need to coordinate and compete for the use of shared resources. At the end of the course the student will have a good understanding of the constructs for concurrent programming and be able to use them to write and analyze concurrent programs.

Ore:

20

Professors:

Rocco De Nicola (IMT Lucca)

Disponibile:

Scientific Writing, Dissemination and Evaluation (long seminar without exam)

Corpo:

TBD

Ore:

8

Professors:

Luca Aceto (Reykjavik University)

Software Engineering for Service-Oriented Systems and Autonomic Systems

Corpo:

Service-Oriented Computing is an emerging paradigm where services are understood as autonomous, platform-independent computational entities that can be described, published, categorised, discovered, and dynamically assembled for developing massively distributed, interoperable, evolvable systems and applications. In this course a model-driven approach to the development of service-oriented software systems is presented where foundational theories and techniques are integrated in a pragmatic software engineering approach. In particular, an introduction to modelling service-oriented systems in a diagrammatic style with UML is given and their formal foundations in terms of process algebra and automata are presented. It will be shown how mathematical models can be generated by model transformations and further used for qualitative and quantitative analysis of service-oriented software.

Ore:

20

Professors:

Francesco Tiezzi (Università degli Studi di Camerino), Martin Wirsing (Ludwig-Maximilians-Universität München)

Disponibile:

Statistics Lab.

Corpo:

- Brief introduction to R (http://www.r-project.org/)

- Creating random variables.

- Applications to the central limit theorem and the law of large numbers

- Descriptive statistics: (i) Representing probability and cumulative distribution functions in discrete and continuous cases; (ii) calculating mean, variance, concentration indexes, covariance and correlation coeff.

- Statistical inference: (i) Point estimation and properties; (ii) interval estimation and properties; (iii) hypothesis testing and properties.

- Theory and applications of simple regression model (model, assumptions, estimation methods, residual diagnostics).

- If time permits:

Theory and applications of Bootstrap and Jacknife elements for simple parameters and for the regression model parameters.

Prerequisites: The topics of ?Foundations of Probability Theory and Statistical inference? are

supposed known.

- Creating random variables.

- Applications to the central limit theorem and the law of large numbers

- Descriptive statistics: (i) Representing probability and cumulative distribution functions in discrete and continuous cases; (ii) calculating mean, variance, concentration indexes, covariance and correlation coeff.

- Statistical inference: (i) Point estimation and properties; (ii) interval estimation and properties; (iii) hypothesis testing and properties.

- Theory and applications of simple regression model (model, assumptions, estimation methods, residual diagnostics).

- If time permits:

Theory and applications of Bootstrap and Jacknife elements for simple parameters and for the regression model parameters.

Prerequisites: The topics of ?Foundations of Probability Theory and Statistical inference? are

supposed known.

Ore:

10

Professors:

Irene Crimaldi (IMT Lucca), Rodolfo Metulini (IMT Lucca)

Stochastic Processes and Stochastic Calculus

Corpo:

This course aims at introducing some important stochastic processes (Markov chains, martingales,

Poisson process, Wiener process) and Ito calculus.

Some proofs are sketched or omitted in order to have more time for examples, applications and

exercises.

In particular, the course deals with the following topics:

- Markov chains (definitions and basic properties, classification of states, invariant measure, stationary distribution, some convergence results and applications, passage problems, random walks, urn models, introduction to the Markov chain Monte Carlo method),

- conditional expectation,

- martingales (definitions and basic properties, Burkholder transform, stopping theorem and some

applications, predictable compensator and Doob decomposition, some convergence results, game theory, random walks, urn models),

- Poisson process,

- Markov processes,

- Wiener process and Ito calculus,

- Ornstein-Uhlenbeck process.

Prerequisites: The topics of ?Foundations of Probability Theory and Statistical Inference? are supposed to be known.

Poisson process, Wiener process) and Ito calculus.

Some proofs are sketched or omitted in order to have more time for examples, applications and

exercises.

In particular, the course deals with the following topics:

- Markov chains (definitions and basic properties, classification of states, invariant measure, stationary distribution, some convergence results and applications, passage problems, random walks, urn models, introduction to the Markov chain Monte Carlo method),

- conditional expectation,

- martingales (definitions and basic properties, Burkholder transform, stopping theorem and some

applications, predictable compensator and Doob decomposition, some convergence results, game theory, random walks, urn models),

- Poisson process,

- Markov processes,

- Wiener process and Ito calculus,

- Ornstein-Uhlenbeck process.

Prerequisites: The topics of ?Foundations of Probability Theory and Statistical Inference? are supposed to be known.

Ore:

30

Professors:

Irene Crimaldi (IMT Lucca), Andrea Gabrielli ( Istituto dei Sistemi Complessi (ISC) - CNR, UOS ", Sapienza", )

Compulsory:

Theory and Numerics of Ordinary and Partial Differential Equations

Corpo:

The first lesson of the course will provide a primer on complex variables. Using this mathematical formalism, the focus of the remaining first part of the course will be to introduce linear ordinary and linear partial differential equations, and the "cheap" methods to solve them using Fourier and Laplace transforms. The ordinary and partial differential equations will be placed into a context of applied mathematics (e.g. classic deterministic and stochastic systems) saving the theoretical approach for advanced lectures. The lecture notes will be provided in Mathematica computable document format, with an emphasis on graphical in-class demonstration.

The second part of the course will introduce PhD students to numerical techniques for the approximate treatment of linear partial differential equations (PDEs) governing physical, engineering and financial problems. The theoretical fundamentals of the finite element method are introduced step-by-step in reference to exemplary model problems related to heat conduction, linear elasticity and pricing of stock options in finance. Special attention is given to the finite element technology and to the implementation of the weak forms into a research code for fast intensive computations.

The second part of the course will introduce PhD students to numerical techniques for the approximate treatment of linear partial differential equations (PDEs) governing physical, engineering and financial problems. The theoretical fundamentals of the finite element method are introduced step-by-step in reference to exemplary model problems related to heat conduction, linear elasticity and pricing of stock options in finance. Special attention is given to the finite element technology and to the implementation of the weak forms into a research code for fast intensive computations.

Ore:

40

Professors:

Alexander Petersen (IMT Lucca), Marco Paggi (IMT Lucca)

Compulsory:

Disponibile: