# Courses

## CDSS 2013-2014

**Courses found: 41**

### Advanced Topics of Computer Science

This course will be organized as series of reading groups or specialized seminars by members or collaborators of the IMT research units

40 Hours

**Professors / Lecturers:** Rocco De Nicola

**Available for Curricula:**
*
Computer Science
(ADV)
;
*

### Advanced Topics of Control Systems

This course will be organized as series of reading groups or specialized seminars by members or collaborators of the IMT research units

20 Hours

**Professors / Lecturers:** Alberto Bemporad

**Available for Curricula:**
*
Systems Science
(ADV)
;
*

### Advanced Topics of Image Analysis

This course will be organized as series of reading groups or specialized seminars by members or collaborators of the IMT research units

20 Hours

**Professors / Lecturers:** Sotirios Tsaftaris

**Available for Curricula:**
*
Image Analysis
(ADV)
;
*

### Advanced Topics of Management Science

This course will be organized as series of reading groups or specialized seminars by members or collaborators of the IMT research units

40 Hours

**Professors / Lecturers:** Massimo Riccaboni / Guido Caldarelli / Andrea Gabrielli, Istituto dei Sistemi Complessi (ISC) - CNR

**Available for Curricula:**
*
Management Sciences
(ADV)
;
*

### Algorithmics

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.

20 Hours

**Professors / Lecturers:** Alberto Lluch Lafuente

**Available for Curricula:**
*
Computer Science
(INT)
;
*
*
Image Analysis
(INT)
;
*

### Basic Numerical Linear Algebra

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.

20 Hours

**Professors / Lecturers:** Luigi Brugnano, Univ. Firenze

**Available for Curricula:**
*
Computer Science
(INT)
;
*
*
Systems Science
(INT)
;
*
*
Image Analysis
(INT)
;
*
*
Management Sciences
(INT)
;
*
*
Economics
(INT)
;
*

### Cloud Computing for Big data Analysis

We will discuss the characteristics and benefits of Clouds, introduce the MapReduce framework based on Hadoop, discuss how to design efficient MapReduce algorithms and present the state-of-the-art in MapReduce algorithms. Furthermore, we will cover the basics and some advanced methods of data analysis in the cloud. In particular, we will present the three fundamental analysis techniques: association rules, clustering, and supervised learning, with a cloud-computing related twist. The main part of the course are the following:

1. Introduction: Distributed computing for Big Data problems, Data Management.

2. Concepts and techniques for Cloud computing: Cloud characteristics, Cloud benefits (elasticity, pay-as-you-go, scale economies), Cloud service models (IaaS, PaaS, SaaS).

3. Programming solutions for Big Data problems: MapReduce programming model and patterns (Hadoop programming), PIG, Streaming Data Analysis in the Cloud.

4. Big Data Analysis Technique: Basic Techniques for Mining Big Data (Association Rules, Clustering, Supervised Learning).

Prerequisites

A basic knowledge of Computer Architectures, Algorithm design, object oriented programming, Principles of Concurrent and Distributed Programming

20 Hours

**Professors / Lecturers:** Claudio Lucchese, CNR/ Fabrizio Silvestri, CNR/ Nicola Tonellotto, CNR

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*
*
Management Sciences
(ADV)
;
*

### Computational Contact and Fracture Mechanics

This course provides a general overview on the theories of contact and fracture mechanics, relevant for a wide range of disciplines ranging from materials science to engineering and geophysics. Introducing their theoretical foundations, the physical aspects of the resulting nonlinearities induced by such phenomena are emphasized. Numerical methods for their approximate solution are also presented, together with a series of applications to real case studies.

The course covers the following topics:

I. Contact mechanics

A. The Hertzian contact between smooth spheres

B. The Cattaneo-Mindlin theory for frictional contact

C. Numerical methods for the treatment of the unilateral contact constraint (the penalty method

and Lagrange multipliers in FEM, the active set strategy in BEM)

D. Contact between rough surfaces: statistical and numerical methods

II. Fracture mechanics

A. Fundamentals of linear elastic fracture mechanics (LEFM), stress-intensity factors

B. Strength and toughness of materials, criteria for crack propagation

C. Examples in LEFM solved with the use of the finite element method

D. Nonlinear fracture mechanics (NLFM): the cohesive zone model (CZM)

E. Numerical implementation of the CZM in the finite element method

F. Applications of NLFM to materials science, retrofitting of civil/architectonic structures,

composite materials

20 Hours

**Professors / Lecturers:** Marco Paggi

**Available for Curricula:**
*
Computer Science
(SUGG)
;
*
*
Systems Science
(ADV)
;
*
*
Image Analysis
(SUGG)
;
*
*
Management Sciences
(SUGG)
;
*
*
Economics
(SUGG)
;
*

### Computational Finance

In the field of quantitative finance, one of the most challenging tasks is the gap between theoretical models and the actual software implementation. Cross some different areas (derivatives evaluation, risk management, accounting issues) several problems arise: discretization, analytical approximation, montecarlo simulation vs. numerical probability, optmization and so on. After a short overview of the main financial areas, the course aims to give some insights on these topics, with a special focus on the risk management current hard problems and the related software algorithms.

20 Hours

**Professors / Lecturers:** Michele Bonollo, Credito Trevigiano

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Management Sciences
(ADV)
;
*
*
Economics
(ADV)
;
*

### Computer Programming and Methodology

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.

20 Hours

**Professors / Lecturers:** Michele Loreti, Univ. Firenze

**Available for Curricula:**
*
Computer Science
(INT)
;
*
*
Systems Science
(INT)
;
*
*
Image Analysis
(INT)
;
*
*
Management Sciences
(INT)
;
*
*
Economics
(INT)
;
*

### Convex Optimization

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.

20 Hours

**Professors / Lecturers:** Panagiotis Patrinos

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*
*
Management Sciences
(ADV)
;
*

### Data Science with Complex Networks

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.

20 Hours

**Professors / Lecturers:** Alessandro Chessa / Guido Caldarelli

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*
*
Management Sciences
(ADV)
;
*
*
Economics
(ADV)
;
*

### Econometrics I

This course presents a comprehensive treatment of econometric methods for linear models and assumes working knowledge of undergraduate econometrics basic linear algebra, basic probability theory, and statistics that are covered in the pre-courses.

40 Hours

**Professors / Lecturers:** Cristina Tealdi / Marco Leonardi, Universita' Cattolica di Milano

**Available for Curricula:**
*
Management Sciences
(ADV)
;
*
*
Economics
(CORE)
;
*

### Econometrics II

I - Overview

This part of the course is designed to provide an introduction to models with limited dependent variables: discrete choice models, truncation, censoring and sample selection. The models presented here require a working knowledge of multivariate calculus, linear algebra, maximum likelihood and least squares estimation. Emphasis is on applications and use rather than methods and each topic is addressed with in-depth Stata examples.

Discrete Choice Models (DCM)

- DCM.1 The linear probability model

- DCM.2 Binary Logit and probit models

- DCM.3 Maximum Likelihood: estimation and test

- DCM.4 Multinomial and ordered response models

Truncation, censoring and sample selection (TCS)

- TCS.1 Censoring and truncation

- TCS.2 Corner solution responses

- TCS.3 Censoring and Sample selection

II - Overview

The course aims at introducing linear models for (stationary) panel data, i.e. repeated observations on the same statistical units. This kind of data are nowadays widely used in several fields so that the knowledge of the

appropriate econometric techniques is necessary for applied researchers. At first, static models will be introduced and the most widely used estimators - alongside with the hypotheses needed for their consistency - will be discussed. Subsequently, dynamic models will be presented, with a necessary digression about GMM (Generalized Method of Moments) estimator, which proves to be the appropriate estimator for this kind of models.

Contents of the classes

Class 1: Static Linear Panel data models. Introduction to Panel Data and main estimators.

Class 2: Static Linear Panel data models. Test of hypotheses and different variance structures. Generalized Method of Moments Estimation. Introduction.

Class 3: Generalized Method of Moments Estimation. Test of hypotheses. Dynamic Linear Panel data models. Introduction and bias of standard estimators.

Class 4: Dynamic Linear Panel data models. Arellano-Bond and Blundell-Bond estimators.

36 Hours

**Professors / Lecturers:** Carla Rampichini, Università degli Studi di Firenze / Valentina Tortolini / Luigi Benfratello, Università degli Studi di Napoli Federico II

**Available for Curricula:**
*
Management Sciences
(ADV)
;
*
*
Economics
(CORE)
;
*

### Essentials of Calculus

The course aims at recalling the fundamental concepts of static optimization.

The basics of calculus (in one and more variables) are supposed to be known.

In particular the course will deal with the following topics.

- Functions of one variable: drawing graph of functions, application of continuity and derivatives to constrained and unconstrained optimization, differentiation and maximization of integral functions.

- Functions of several variables: partial derivatives, methods for constrained and unconstrained optimization, optimization of implicit and integral functions.

20 Hours

**Professors / Lecturers:** Alexander Petersen / Orion Penner

**Available for Curricula:**
*
Computer Science
(INT)
;
*
*
Systems Science
(INT)
;
*
*
Image Analysis
(INT)
;
*
*
Management Sciences
(INT)
;
*
*
Economics
(INT)
;
*

### Ethics and Research: Objectivity, Neutrality and Values in Science (long seminar)

The idea that science – “pure” science, that is, as opposed to “applied” science, that is technology – is a morally neutral enterprise is often presented as a matter of fact. It is obviously so, it is argued, because the task of science, as we understand it, is that of explaining phenomena, not that of telling how phenomena should be. And it is importantly so, because any interference of values (broadly conceived) in the scientific discourse would entail the subordination of the search for truth, which is taken to be as the aim of science, to politics, religion, or any metaphysical framework. The well known case of Galileo (1564-1642), or that of Lysenko (1598-1976) – however different they were – are all too clear warnings about what might happen if scientific research were to depend on values alien to it.

In this series of seminars students will be exposed to and challenged by a different view. According to a growing number of philosophers of science, the commitment to values is inescapable for scientists, for values are indeed part and parcel of scientific research. In order to prevent cases such as those of Galileo and Lysenko from happening again, however, the often conflated ideas of “objectivity” and “neutrality” (scientific knowledge must be neutral in order to be objective, that is) must be clearly distinguished. Furthermore, it may be argued that any attempt to free scientists from any moral responsibility would not only prevent them from achieving the independence they rightly strive for, but would seriously challenge it.

Structure of the “long seminar” (preliminary): 2-3 introductory lectures and a series of seminars (presentation of papers by students + discussion).

10 Hours

**Professors / Lecturers:** Stefano Gattei

**Available for Curricula:**
*
Computer Science
(SUGG)
;
*
*
Systems Science
(SUGG)
;
*
*
Image Analysis
(SUGG)
;
*
*
Management Sciences
(SUGG)
;
*
*
Economics
(SUGG)
;
*
*
Management and Development of Cultural Heritage
(SUGG)
;
*
*
Political History
(SUGG)
;
*

### Formal Methods for Computer Science

This course is intended to acquaint new students with the computer science requirements of the PhD program and with some of the research areas that are active within the program. It aims at providing the basic mathematical techniques necessary for understanding semantics and logics of programming languages, which are at the basis of different kinds of program analysis. The main topics that will be considered are fundamental mathematical tools, such as basic set theory, induction principles and fix point theory; basic notions of logical reasoning; and the main approaches to semantics of programming languages, namely structural operational semantics and denotational semantics.

20 Hours

**Professors / Lecturers:** Valerio Senni, Rocco de Nicola

**Available for Curricula:**
*
Computer Science
(INT)
;
*

### Foundations of Probability Theory

This course aims at introducing from an advanced point of view the fundamental concepts of probability theory. Moreover, various forms of convergence are introduced and studied and some important limit results and tools (such as the Fourier transform/characteristic function) are illustrated. At the beginning of the course some elements of measure theory and integration theory (the Lebesgue integral) are given.

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

20 Hours

**Professors / Lecturers:** Irene Crimaldi

**Available for Curricula:**
*
Computer Science
(INT)
;
*
*
Systems Science
(INT)
;
*
*
Image Analysis
(INT)
;
*
*
Management Sciences
(INT)
;
*
*
Economics
(INT)
;
*

### Game Theory

Mechanism Design. Revelation principle, Dominance and Nash Implementation.

Strategic and Axiomatic Bargaining.

Asymmetric Information and Optimal Contracts. Moral Hazard and Adverse Selection models.

Signaling and Screening Models. Applications.

Static games of complete information: definition of a game; normal form representation; strongly and weakly dominated strategies; Nash Equilibrium (NE); mixed strategy equilibrium. Applications of NE and introduction to market competition; Cournot competition; Bertrand competition; externalities; public goods.

Dynamic games of complete information: definition of a dynamic game; extensive form representation; perfect and imperfect information; Backward Induction equilibrium; Subgame Perfect equilibrium. Repeated games: Definition; one-shot deviation property; folk theorem; application to Rubinstein bargaining. Static games of incomplete information: Bayesian games; Bayesian Nash equilibrium. Dynamic games of incomplete information: perfect Bayesian equilibrium; signalling games, cheap talk.

30 Hours

**Professors / Lecturers:** Nicola Dimitri, Univ. Siena

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Management Sciences
(ADV)
;
*
*
Economics
(CORE)
;
*

### Intellectual Property and Management of Research (long seminar)

1. A short introduction to the funding schemes of Horizon 2020

1.1 Support of frontier research by the European Research Council:

Starting and Advanced Grants, Proof of Concept, Synergy Grant

1.2 Support of future and emerging technology: FET flagships initiatives

1.3 Training and career perspectives of researchers: Marie Curie actions

2. Other funding opportunities in EU and USA: the Alexander von Humboldt Foundation and the Deutscher Akademischer Austausch Dienst for Germany; Fulbright scholarships for USA; the Royal Society in UK; joint cooperation projects with France.

3. How to write a budget of a proposal.

4. How to manage your granted project.

Some seminars by invited experts will be offered.

15 Hours

**Professors / Lecturers:** Marco Paggi

**Available for Curricula:**
*
Computer Science
(SUGG)
;
*
*
Systems Science
(SUGG)
;
*
*
Image Analysis
(SUGG)
;
*
*
Management Sciences
(SUGG)
;
*
*
Economics
(SUGG)
;
*
*
Management and Development of Cultural Heritage
(SUGG)
;
*
*
Political History
(SUGG)
;
*

### Introduction to Mathematical Statistics and Stochastic Processes

This course aims at introducing the concept of statistical inference and the notion of stochastic process. Some basics of mathematical statistics are given and Markov chains, Poisson process and martingales are studied.

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

Prerequisites: The topics of “Foundations of Probability Theory” are supposed to be known.

20 Hours

**Professors / Lecturers:** Irene Crimaldi

**Available for Curricula:**
*
Computer Science
(INT)
;
*
*
Systems Science
(INT)
;
*
*
Image Analysis
(INT)
;
*
*
Management Sciences
(INT)
;
*
*
Economics
(INT)
;
*

### Introduction to Networks Theory

TBD

10 Hours

**Professors / Lecturers:** Guido Caldarelli

**Available for Curricula:**
*
Computer Science
(INT)
;
*
*
Systems Science
(INT)
;
*
*
Image Analysis
(INT)
;
*
*
Management Sciences
(INT)
;
*
*
Economics
(INT)
;
*
*
Management and Development of Cultural Heritage
(INT)
;
*

### Introduction to Stochastic Control Theory and Applications

Aims

The course is to provide students with an overview of the main methods and recent developments in the area of stochastic control and their applications to economics.

Contents

Classical approach to stochastic control problem by dynamic programming methods. Viscosity solutions and stochastic control.

20 Hours

**Professors / Lecturers:** Andrea Vindigni / Simone Scotti, Università di Torino

**Available for Curricula:**
*
Systems Science
(ADV)
;
*
*
Management Sciences
(ADV)
;
*
*
Economics
(ADV)
;
*

### Large Scale Image Analysis for Natural and Life Sciences

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.

20 Hours

**Professors / Lecturers:** Sotirios Tsaftaris / Rohan Dharmakumar,Biomedical Imaging Research Institute (BIRI), Cedars-Sinai, LA

**Available for Curricula:**
*
Image Analysis
(ADV)
;
*

### Machine Learning and Pattern Recognition

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.

20 Hours

**Professors / Lecturers:** Sotirios Tsaftaris

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*

### Management

Applications of quantitative techniques to managerial decisions (data-driven decision making). Topics include applications of data mining, machine learning, statistical models, predictive analytics, econometrics, optimization, risk analysis, decision theory, data visualization and business communication in finance, marketing, operations, R&D, business intelligence and other business areas generating and consuming large amounts of data.

10 Hours

**Professors / Lecturers:** Massimo Riccaboni

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Management Sciences
(ADV)
;
*
*
Economics
(ADV)
;
*

### Management (Basics)

TBD

10 Hours

**Professors / Lecturers:** Massimo Riccaboni

**Available for Curricula:**
*
Computer Science
(INT)
;
*
*
Systems Science
(INT)
;
*
*
Management Sciences
(INT)
;
*
*
Economics
(INT)
;
*
*
Management and Development of Cultural Heritage
(INT)
;
*

### Management of Complex Systems: Approaches to Problem Solving

Methods and approach to problem solving. Problem analysis; analysis of complex systems (related to cultural heritage, such as a city of art organization, promotion, etc.). The course will include practical simulations. The course will be linked to a seminar on specific Case studies

40 Hours

**Professors / Lecturers:** Andrea Zocchi, Esselunga S.P.A.
Dario Cacciatore, McKinsey & Company, Inc. Italy

**Available for Curricula:**
*
Computer Science
(SUGG)
;
*
*
Systems Science
(SUGG)
;
*
*
Management Sciences
(SUGG)
;
*
*
Management and Development of Cultural Heritage
(CORE)
;
*

### Mobile and online social networking

The course will firstly focus on models and experimental evidence about the social structures (and their dynamic evolution over time) that human build when interacting both offline and online, with special emphasis on Online Social Networks platforms (Facebook and Twitter in particular). Then, it will focus on Mobile Social Networks. It will focus on the key networking enablers for Mobile Social Networing services leveraging direct interactions between users due to physical mobility. The course will cover relevant mobility models, and techniques to route and disseminate content in this type of mobile networks. It will present both foundational results, as well as practical protocols and solutions for these problems.

20 Hours

**Professors / Lecturers:** Andrea Passarella, CNR / Chiara Boldrini, CNR

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*

### Model Checking

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.

20 Hours

**Professors / Lecturers:** Alberto Lluch Lafuente/Jost-Pieter Katoen, RWTH Aachen University

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*

### Model Predictive Control

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.

20 Hours

**Professors / Lecturers:** Alberto Bemporad

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*
*
Management Sciences
(ADV)
;
*

### Modelling and Verification of Reactive Systems

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.

20 Hours

**Professors / Lecturers:** Rocco De Nicola

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*

### Networks Theory

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.

20 Hours

**Professors / Lecturers:** Guido Caldarelli / Massimo Riccaboni

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*
*
Management Sciences
(ADV)
;
*
*
Economics
(ADV)
;
*

### Numerical Methods for the Solution of PDEs

This course introduces 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 course covers the following topics:

I. Heat conduction

A. Strong and weak forms

B. Finite element approximation

C. Isoparametric shape functions and numerical integration

D. Transient analysis

E. Numerical implementation

F. Examples

II. Option pricing in finance

A. The Black-Scholes-Merton model: strong and weak forms

B. Finite element approximation

C. Numerical implementation

D. Examples

III. Linear elasticity

A. The minimum potential energy theorem

B. The displacement finite element method

C. Finite element discretization in 2D and 3D, numerical integration

D. Examples in materials science and structural mechanics

20 Hours

**Professors / Lecturers:** Marco Paggi

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*
*
Management Sciences
(ADV)
;
*
*
Economics
(ADV)
;
*

### Optimal Control

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.

20 Hours

**Professors / Lecturers:** Giorgio Gnecco

**Available for Curricula:**
*
Computer Science
(INT)
;
*
*
Systems Science
(INT)
;
*
*
Image Analysis
(INT)
;
*
*
Management Sciences
(INT)
;
*
*
Economics
(INT)
;
*

### Principles of Concurrent and Distributed Programming

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.

20 Hours

**Professors / Lecturers:** Rocco De Nicola

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*

### Quantitative Finance

The course covers important topics in modern quantitative finance and risk management: efficient market hypothesis and violations, financial markets micro-structure and types of arbitrage, general principles of modelling the price dynamics of financial assets, market risk and other types of financial risks, Value-at-Risk (VaR) approach and applications, modelling of extreme events and crisis, VaR analysis for financial derivatives, copula methods,modelling of trends in time series in connection with technical analysis, and the foundations of high-frequency arbitrage trading. This course will enable the students to develop both theoretical knowledge and practical skills to analyze modern financial markets.

20 Hours

**Professors / Lecturers:** Roberto Reno', Universita' di Siena

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*
*
Management Sciences
(ADV)
;
*
*
Economics
(ADV)
;
*

### Scientific Writing, Dissemination and Evaluation (long seminar)

TBD

6 Hours

**Professors / Lecturers:** Luca Aceto, Reykjavik University

**Available for Curricula:**
*
Computer Science
(SUGG)
;
*
*
Systems Science
(SUGG)
;
*
*
Image Analysis
(SUGG)
;
*
*
Management Sciences
(SUGG)
;
*
*
Economics
(SUGG)
;
*
*
Management and Development of Cultural Heritage
(SUGG)
;
*
*
Political History
(SUGG)
;
*

### Software Engineering for Service-Oriented and Autonomic Systems

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.

20 Hours

**Professors / Lecturers:** Francesco Tiezzi / Martin Wirsing, LMU Munich

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Image Analysis
(ADV)
;
*

### Statistics Lab

• Brief intro to R

• 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.

• Inferential statistics: (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

• Theory and applications of Logit, Probit , count data (poisson, negative binomial) regression models

Recommended prerequisites:

• Course in Foundations of Probability Theory (I. Crimaldi)

• Course in Introduction to Mathematical Statistics and Stochastic Processes (I. Crimaldi)

• Very basic R knowledge (http://www.r-project.org/)

R packages:

Boot, bootstrap, stats, base (more to come)

10 Hours

**Professors / Lecturers:** Rodolfo Metulini

**Available for Curricula:**
*
Computer Science
(INT)
;
*
*
Systems Science
(INT)
;
*
*
Image Analysis
(INT)
;
*
*
Management Sciences
(INT)
;
*
*
Economics
(INT)
;
*

### Timed Automata and Logics for Real-Time Systems

The main aim of this course is to provide an introduction to timed automata, a fundamental formalism for the modelling and verification of real-time systems introduced by Rajeev Alur and David Dill in the late 1980s, and to some of the logics that have been proposed for describing properties of such systems. As part of the course, we will also introduce the model checker UPPAAL (http://www.uppaal.org/), which is the foremost tool suite for the automatic verification of real-time systems and for which its prime architects have received the CAV Award 2013 (http://www.idea4cps.dk/download/uppaalcavaward13.pdf). This part of the course will cover both the theory of timed automata and its applications, and the students will put the theory into practice by working on some projects involving the use of UPPAAL.

If time allows, the course will conclude with a brief introduction to hybrid systems (http://en.wikipedia.org/wiki/Hybrid_system#Tools), which are computing systems such as embedded systems that exhibit both continuous and discrete dynamic behaviour. We will introduce the model of hybrid automata and the basic (un)decidability results for verification problems for that model.

20 Hours

**Professors / Lecturers:** Luca Aceto, Reykjavik University

**Available for Curricula:**
*
Computer Science
(ADV)
;
*
*
Systems Science
(ADV)
;
*