Degree Course: Management and automation engineering
A.Y. 2020/2021
Autonomia di giudizio
Nell'ambito delle aree di propria competenza, i laureati saranno in grado di assumere responsabilità decisionali autonome in progetti anche di grandi dimensioni e di partecipare attivamente al processo decisionale in contesti anche interdisciplinari.
Questo obiettivo sarà perseguito attraverso alcuni corsi di insegnamento con componente progettuale e attraverso la tesi di laurea magistrale.
Esso sarà verificato attraverso i relativi esami di profitto e l'esame di laurea magistrale.Abilità comunicative
I laureati magistrali saranno in grado di comunicare efficacemente e interagire sulle tematiche di interesse con interlocutori specialisti e non specialisti, anche di alto livello.
Questo obiettivo sarà perseguito attraverso gli esami ed il tirocinio.
Esso sarà verificato attraverso gli esami scritti e orali e attraverso l'esame finale di laurea.
Esso sarà verificato attraverso i relativi esami di profitto e l'esame di laurea magistrale.Capacità di apprendimento
I laureati saranno in grado di procedere in maniera autonoma nell'aggiornamento professionale e, nel caso degli studenti migliori, nella ricerca scientifica.
Questo obiettivo sarà perseguito attraverso alcuni specifici corsi di insegnamento, che prevedono una componente seminariale e di ricerca bibliografica, e attraverso la tesi di laurea magistrale.
Esso sarà verificato attraverso i relativi esami di profitto e l'esame di laurea magistrale.Requisiti di ammissione
Per accedere proficuamente al Corso di Laurea Magistrale in Ingegneria gestionale e dell'Automazione, lo studente deve:
- conoscere adeguatamente gli aspetti metodologico-operativi della matematica e delle altre scienze di base ed essere capace di utilizzare tale conoscenza per interpretare e descrivere i problemi dell'Ingegneria;
- conoscere adeguatamente gli aspetti metodologico-operativi delle scienze dell'Ingegneria, sia in generale, sia in modo approfondito relativamente all' area specifica dell'Ingegneria Informatica nella quali deve avere capacità di identificare, formulare e risolvere i problemi utilizzando metodi, tecniche e strumenti aggiornati;
- essere capace di utilizzare tecniche e strumenti per la progettazione di componenti, sistemi, processi;
- essere capace di condurre esperimenti e di analizzarne e interpretarne i dati;
- essere capace di comprendere l'impatto delle soluzioni ingegneristiche nel contesto sociale e conoscere i contesti aziendali e la cultura d'impresa nei suoi aspetti economici, gestionali e organizzativi;
- conoscere i contesti contemporanei e le proprie responsabilità professionali ed etiche;
- essere capace di comunicare efficacemente, in forma scritta e orale, in lingua Inglese;
- possedere gli strumenti cognitivi di base per l'aggiornamento delle proprie conoscenze.
Il Regolamento Didattico del Corso di Laurea Magistrale (reperibile al link indicato) descrive le modalità di verifica puntuale di tali conoscenze.Prova finale
La prova finale è costituita dalla discussione di una tesi originale, elaborata in modo autonomo dallo studente sotto la guida di un relatore ed eventualmente di uno o più co-relatori.Orientamento in ingresso
Le azioni di orientamento in ingresso sono improntate alla realizzazione di processi di raccordo con la scuola media secondaria.
Si concretizzano in attività di carattere informativo sui Corsi di Studio (CdS) dell'Ateneo ma anche come impegno condiviso da scuola e università per favorire lo sviluppo di una maggiore consapevolezza da parte degli studenti nel compiere scelte coerenti con le proprie conoscenze, competenze, attitudini e interessi.
Le attività promosse si articolano in:
a) autorientamento;
b) incontri e manifestazioni informative rivolte alle future matricole;
c) sviluppo di servizi online e pubblicazione di guide sull'offerta formativa dei CdS.
Tra le attività svolte in collaborazione con le scuole per lo sviluppo di una maggiore consapevolezza nella scelta, il progetto di autorientamento è un intervento che consente di promuovere un raccordo particolarmente qualificato con alcune scuole medie superiori.
Il progetto, infatti, è articolato in incontri svolti presso le scuole ed è finalizzato a sollecitare nelle future matricole una riflessione sui propri punti di forza e sui criteri di scelta.
La presentazione dell'offerta formativa agli studenti delle scuole superiori prevede tre eventi principali distribuiti nel corso dell'anno accademico ai quali partecipano tutti i CdS.
- Salone dello studente, si svolge presso la fiera di Roma fra ottobre e novembre e coinvolge tradizionalmente tutti gli Atenei del Lazio e molti Atenei fuori Regione, Enti pubblici e privati che si occupano di Formazione e Lavoro.
Roma Tre partecipa a questo evento con un proprio spazio espositivo, con conferenze di presentazione dell'offerta formativa dell'Ateneo e promuove i propri Dipartimenti scientifici grazie all'iniziativa Roma 1,2,3 … Scienze;
- Giornate di Vita Universitaria (GVU), si svolgono ogni anno da dicembre a marzo e sono rivolte agli studenti degli ultimi due anni della scuola secondaria superiore.
Si svolgono in tutti i Dipartimenti dell'Ateneo e costituiscono un'importante occasione per le future matricole per vivere la realtà universitaria.
Gli incontri sono strutturati in modo tale che accanto alla presentazione dei Corsi di Laurea, gli studenti possano anche fare un'esperienza diretta di vita universitaria con la partecipazione ad attività didattiche, laboratori, lezioni o seminari, alle quali partecipano anche studenti seniores che svolgono una significativa mediazione di tipo tutoriale.
Partecipano annualmente circa 5.000 studenti;
- Orientarsi a Roma Tre, rappresenta la manifestazione che chiude le annuali attività di orientamento in ingresso e si svolge in Ateneo a luglio di ogni anno.
L'evento accoglie, perlopiù, studenti romani che partecipano per mettere definitivamente a fuoco la loro scelta universitaria.
Durante la manifestazione viene presentata l'offerta formativa e sono presenti, con un proprio spazio, tutti i principali servizi di Roma Tre, le segreterie didattiche e la segreteria studenti.
I servizi online messi a disposizione dei futuri studenti universitari nel tempo sono aumentati tenendo conto dello sviluppo delle nuove opportunità di comunicazione tramite web.
Inoltre, durante tutte le manifestazioni di presentazione dell'offerta formativa, sono illustrati quei servizi online (siti web di Dipartimento, di Ateneo, Portale dello studente etc.) che possono aiutare gli studenti nella loro scelta.Il Corso di Studio in breve
Il Corso di Laurea Magistrale in Ingegneria Gestionale e dell'Automazione, afferente al Dipartimento di Ingegneria dell' Università degli Studi Roma Tre e appartenente alla classe delle Lauree Magistrali in Ingegneria Informatica LM-32, è finalizzato al conseguimento del titolo di studio universitario: Laurea Magistrale in Ingegneria Gestionale e dell'Automazione.
Il Corso di Laurea Magistrale mira a formare ingegneri informatici con elevata qualificazione professionale, specializzati nelle applicazioni gestionali e dell'automazione.
Risponde quindi a un'esigenza precisa del territorio, proveniente sia dai laureati in Ingegneria Informatica che vogliono approfondire la preparazione gestionale e dell'automazione, presente in misura embrionale nelle lauree triennali in Ingegneria Informatica, sia dalle imprese per coprire quelle posizioni di esperto informatico con competenze multidisciplinari gestionali e dell'automazione che sono richieste dal mercato del lavoro.
Il corso di studio è ad accesso libero, senza numero programmato, ed il requisito richiesto è il possesso di una laurea nella Classe delle Lauree in Ingegneria dell'Informazione o nella Classe delle Lauree in Ingegneria Industriale.
Inoltre, è necessario che lo studente abbia competenze di: analisi matematica, geometria ed algebra, fisica, ricerca operativa, fondamenti di informatica, fondamenti di automatica, sistemi ad eventi, elettrotecnica, economia applicata all'Ingegneria, tipiche dei corsi di laurea in Ingegneria Informatica.
Pertanto, per accedere al corso di studio è necessario presentare una domanda di pre-iscrizione, documentando tutte le attività formative del proprio piano di studio relativo alla Laurea.
Il corso di studio è organizzato con (i) un primo anno dedicato al consolidamento e al rafforzamento della formazione ingegneristica di primo livello nel campo gestionale e in quello dell'automazione e (ii) un secondo anno, dedicato all'acquisizione di conoscenze avanzate e d'avanguardia nell'ambito della ingegneria informatica, gestionale e dell'automazione.
L'approfondimento delle conoscenze nelle aree Gestionale o dell'Automazione viene completato nell'ambito di appositi curriculum formativi e può essere perseguito anche attraverso esperienze internazionali (ad esempio, Erasmus, ecc.) e/o importanti attività di progettazione e ricerca in collegamento con il mondo del lavoro.
Il corso di studio consente l'accesso, previo superamento dell'Esame di Stato, all'Albo professionale dell'Ordine degli Ingegneri nella Sezione A, Settore dell'informazione, ed è orientato alla formazione di tecnici aventi le competenze richieste per operare in numerose realtà lavorative, incluse le industrie operanti negli ambiti della produzione di beni e servizi, le imprese informatiche, elettroniche, elettromeccaniche, in cui sono sviluppate funzioni di dimensionamento e realizzazione di architetture complesse, di sistemi automatici, di processi e impianti per l'automazione, la pubblica amministrazione, gli enti di ricerca, le imprese manifatturiere e di servizi, per l'approvvigionamento e la gestione dei materiali, per l'organizzazione delle attività, per l'organizzazione e l'automazione dei sistemi produttivi, per la logistica e il project management.
Il percorso di studi è comunque progettato per fornire tutte le competenze e conoscenze necessarie per consentire l'accesso ed una proficua fruizione di eventuali successivi corsi di dottorato di ricerca o master di secondo livello.
Il Collegio favorisce il coinvolgimento degli studenti in attività formative presso istituzioni universitarie estere, ad esempio tramite programmi Erasmus o attraverso lo svolgimento del lavoro di tesi presso aziende, università o enti di ricerca esteri.
Lo studente espliciterà le proprie scelte al momento della presentazione,
tramite il sistema informativo di ateneo, del piano di completamento o del piano di studio individuale,
secondo quanto stabilito dal regolamento didattico del corso di studio.
Gestionale
FIRST YEAR
First semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20810208 -
Decision Support Systems and Analytics
(objectives)
The aim of the course is to present the main theoretical and methodological tools for modeling decisions and for identifying the best decision support strategies. The course also aims at providing the skills on how to use the available data in analytical prescriptive models, how to read the results provided by the adopted models and how to interpret them to propose appropriate solutions to complex management problems.
|
6
|
MAT/09
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20802112 -
SIMULATION OF INDUSTRIAL AND LOGISTIC PROCESSES
(objectives)
It gives a formal instruments to model information flows and to optimize the operation management of production systems, in particular of flexible manufacturing systems.
|
9
|
ING-INF/04
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810204 -
Dynamics and Control of Complex Systems
(objectives)
Provide to the students methodologies and techniques for the analysis and modeling of linear time-invariant systems by focusing on the state-space representation. Provide the knowledge for the design of feedback control systems. Derive the state-space model of Multi-Input Multi-Output systems. Provide the knowledge of the structural properties of MIMO dynamical models and the asymptotic observer for the eigenvalue assignment problem and the regulation problem. Provide the students with basic concepts for the analysis of nonlinear system.
|
9
|
ING-INF/04
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
Optional Group:
I ANNO "UNO A SCELTA TRA" PER ENTRAMBI I CURRICULA - (show)
|
6
|
|
|
|
|
|
|
|
|
20801758 -
DATABASES I
(objectives)
Presentation of models, methods and tools for the definition, design and development of software systems that manage large sets of data. A student who has passed the course will be able to: (i) develop software applications that make use of databases of even high complexity, (i) design and built autonomously databases of medium complexity, and (iii) be involved in the project and development of large databases of high complexity.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810087 -
MACHINE LEARNING
(objectives)
Enable students to deepen the main Machine Learning models and methods, such as Regression, Classification, Clustering, Deep Learning, and use them as tools for the development of innovative technologies.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810205 -
Digital entrepreneurship
(objectives)
Provide students with technical and methodological skills
necessary to conceive, develop and implement a digital business project.
The course will be divided into three parts. The first part aims to explain the reasons behind the success of digital companies (especially, but not only, startups) and digital innovation dynamics. The second part offers students the technical and methodological tools for the realization of a digital business project. The third part consists in the realization of a project and is characterized by a strongly experimental approach.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810211 -
Algorithms for big data
|
Also available in another semester or year
|
|
|
Optional Group:
I ANNO "DUE A SCELTA TRA "PER ENTRAMBI I CURRICULA - (show)
|
12
|
|
|
|
|
|
|
|
|
20810158 -
Model Identification and Data Analysis
|
Also available in another semester or year
|
|
20810206 -
SYSTEMS IOT FOR LARGE INFRASTRUCTURES
|
Also available in another semester or year
|
|
20801759 -
FUZZY CONTROL
(objectives)
Provide both methodological knowledge and practical skills on fuzzy logic and possibility theory focusing on the realization of process controllers and data processing for sensor data
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801966 -
INDUSTRIAL PRODUCTION MANAGEMENT
|
Also available in another semester or year
|
|
20801761 -
ELEMENTS OF ORGANISATION
(objectives)
Provide the notions and develop the logics necessary to understand the formal description and the actual functioning of firms and institutions, and their evolutionary tendencies related to the evolution of their operating environment. Introducing to organizational analysis, bringing the student to be able to think about the relationships between market, strategy, structure and processes from a total quality perspective and taking into account people's organizational behaviors and motivations.
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
|
Optional Group:
altre attività offerte e cfu a scelta libera dello studente curriculum gestionale - (show)
|
12
|
|
|
|
|
|
|
|
|
20810209 -
Complex robotic systems laboratory
(objectives)
Provide students with skills necessary to conceive, develop and complete a complex robotics project. The teaching is characterized by a highly experimental approach and will take place at the Robotics Laboratory of the Department of Engineering.
|
3
|
ING-INF/04
|
27
|
-
|
-
|
-
|
Elective activities
|
ITA |
|
20810210 -
Laboratory di metodi decisionali
(objectives)
Provide students with skills necessary to conceive, develop and complete a complex Operations Research project. The teaching is characterized by a highly experimental approach and will take place at the “Automation and operations research in industry” Laboratory of the Department of Engineering
|
3
|
MAT/09
|
27
|
-
|
-
|
-
|
Elective activities
|
ITA |
|
Second semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20802073 -
Optimization of Public Services
(objectives)
This course gives a compendium of techniques, methods and solution
approaches to support the decision making process in the public sector. The
course is based on case studies concerning the design and the management of
public services and it is focused on the development of optimization models
and solution algorithms. Ethical and political issues, typical of the public
sector, are also addressed
|
6
|
MAT/09
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
Optional Group:
I ANNO "DUE A SCELTA TRA "PER ENTRAMBI I CURRICULA - (show)
|
12
|
|
|
|
|
|
|
|
|
20810158 -
Model Identification and Data Analysis
(objectives)
Introduce the student to the fundamentals of system identification applied to sampled systems (ARX and ARMAX model, ordinary least squares, recursive least squares, bayesian filtering). Introduce the student to sensor fusion. To familiarize the student with the use of the MatLab identification toolbox
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810206 -
SYSTEMS IOT FOR LARGE INFRASTRUCTURES
(objectives)
The objectives will be the study and comprehension of systems of system theory and the modeling of large infrastructures (distribution networks, telecommunication networks, transportation networks). Interdependencies among different infrastructures will be analyzed with the aim of evaluating the distributed risk and designing resilient systems. Service oriented architectures will be studied as well as distributed awareness systems.
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801759 -
FUZZY CONTROL
|
Also available in another semester or year
|
|
20801966 -
INDUSTRIAL PRODUCTION MANAGEMENT
(objectives)
This course is aimed at providing the basic methodological tools required for production planning and control in manufacturing systems. Specific methods used in make to stock, assemble to order, make to order, and engineering to order are analyzed, also discussing the differences between push and pull production systems. The course follows the traditional hierarchical approach including aggregate production and capacity planning, master production scheduling, materials and manufacturing resources requirements planning (MRP and CRP techniques), order release planning and job scheduling. furthermore, techniques for demand forecasting and implementation of just in time lean manufacturing systems are presented. The course also provides tools to estimate the performances of manufacturing systems, i.e. the links between work in process, throughput and cycle time, including variability effects and lot sizing decisions. finally, production planning decisions are put in perspective with strategic decisions, with capacity planning issues and with inventory management problems.
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801761 -
ELEMENTS OF ORGANISATION
|
Also available in another semester or year
|
|
|
Optional Group:
I ANNO "UNO A SCELTA TRA" PER ENTRAMBI I CURRICULA - (show)
|
6
|
|
|
|
|
|
|
|
|
20801758 -
DATABASES I
|
Also available in another semester or year
|
|
20810087 -
MACHINE LEARNING
|
Also available in another semester or year
|
|
20810205 -
Digital entrepreneurship
(objectives)
Provide students with technical and methodological skills
necessary to conceive, develop and implement a digital business project.
The course will be divided into three parts. The first part aims to explain the reasons behind the success of digital companies (especially, but not only, startups) and digital innovation dynamics. The second part offers students the technical and methodological tools for the realization of a digital business project. The third part consists in the realization of a project and is characterized by a strongly experimental approach.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810211 -
Algorithms for big data
(objectives)
In many application contexts huge volumes of data are produced which are
used in the economic-financial, political, social and even institutional
fields. Often the data is stored in huge distributed clouds and is
sometimes generated according to a continuous flow, so large as to make
complete storage unfeasible. In many cases the data pertains to entities
in close relationship with each other and gives rise to massive networks
of connections. Familiar examples for such networks are biological and
social networks, distribution networks, and the Web graph. Furthermore,
the fact that the data is stored in systems managed by third parties
poses integrity problems, which have not been considered in the
classical IT literature in terms of both their type and scale.
This scenario poses unprecedented algorithmic challenges, which are
being considered by a vast audience of researchers. In the last decade,
this effort has produced many innovations on both the methodological and
technological level. This course aims at transferring to the students
some of the most important methodological tools originated from the
research on Big Data algorithms. These methodological tools are
presented within challenging application contexts.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
|
20810224 -
MACROEONOMICS
(objectives)
This Course gives students a thorough understanding of macroeconomics by taking a unified view of the subject, allowing connections to be made between the short, medium and long run. By using this unified structure we will analyse the macroeconomic effects of the last financial crisis.
|
9
|
SECS-P/02
|
90
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
SECOND YEAR
First semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20801784 -
LOGISTICS OPTIMISATION
(objectives)
The objective of the course is to endow the students with advanced knowledge for operations planning and scheduling in manufacturing and logistics systems. Topics include deterministic operations research methodology for the design of decision support systems, modeling, algorithms and applications.
|
9
|
MAT/09
|
81
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20801762 -
SUPPORT METHODS FOR MANAGERIAL DECISIONS
(objectives)
Give operative knowledge and methods to organize and manage the managerial activities in systematic mode in all phases that characterize the corporate decision-making. definition of the problem, its formalization. definition of constraints, objectives and alternative, development of algorithms and methods for solution, evaluation, implementation and how to certify solutions and procedures found.
|
9
|
ING-INF/04
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20802137 -
Company Economics and Strategy
(objectives)
Basic knowledge of the financial economic language by which the internal and external company relations and strategies are expressed. Developing of economic and financial evaluation of company operations. Analysis of interactions among internal organizational structures and analysis of financial and economis results drivers and measures.
|
6
|
ING-IND/35
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
Second semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20801786 -
FINAL EXAM
(objectives)
https://ingegneria.uniroma3.it/didattica/tesi-ed-esame-di-laurea/norme-comuni/
https://ingegneria.uniroma3.it/didattica/collegio-informatica/lauree-e-tirocini/laurea-magistrale/
|
26
|
|
-
|
-
|
-
|
-
|
Final examination and foreign language test
|
ITA |
|
20801785 -
SKILLS FOR THE WORLD OF WORK
(objectives)
Frequency seminars mandatory
|
1
|
|
24
|
-
|
-
|
-
|
Other activities
|
ITA |
Automazione dei sistemi complessi
FIRST YEAR
First semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20810204 -
Dynamics and Control of Complex Systems
(objectives)
Provide to the students methodologies and techniques for the analysis and modeling of linear time-invariant systems by focusing on the state-space representation. Provide the knowledge for the design of feedback control systems. Derive the state-space model of Multi-Input Multi-Output systems. Provide the knowledge of the structural properties of MIMO dynamical models and the asymptotic observer for the eigenvalue assignment problem and the regulation problem. Provide the students with basic concepts for the analysis of nonlinear system.
|
9
|
ING-INF/04
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20802112 -
SIMULATION OF INDUSTRIAL AND LOGISTIC PROCESSES
(objectives)
It gives a formal instruments to model information flows and to optimize the operation management of production systems, in particular of flexible manufacturing systems.
|
9
|
ING-INF/04
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Optional Group:
I ANNO AUTOMAZIONE DEI SISTEMI COMPLESSI -UNO A SCELTA - (show)
|
6
|
|
|
|
|
|
|
|
|
20810208 -
Decision Support Systems and Analytics
(objectives)
The aim of the course is to present the main theoretical and methodological tools for modeling decisions and for identifying the best decision support strategies. The course also aims at providing the skills on how to use the available data in analytical prescriptive models, how to read the results provided by the adopted models and how to interpret them to propose appropriate solutions to complex management problems.
|
6
|
MAT/09
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20802073 -
Optimization of Public Services
|
Also available in another semester or year
|
|
|
Optional Group:
I ANNO "UNO A SCELTA TRA" PER ENTRAMBI I CURRICULA - (show)
|
6
|
|
|
|
|
|
|
|
|
20801758 -
DATABASES I
(objectives)
Presentation of models, methods and tools for the definition, design and development of software systems that manage large sets of data. A student who has passed the course will be able to: (i) develop software applications that make use of databases of even high complexity, (i) design and built autonomously databases of medium complexity, and (iii) be involved in the project and development of large databases of high complexity.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810087 -
MACHINE LEARNING
(objectives)
Enable students to deepen the main Machine Learning models and methods, such as Regression, Classification, Clustering, Deep Learning, and use them as tools for the development of innovative technologies.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810205 -
Digital entrepreneurship
(objectives)
Provide students with technical and methodological skills
necessary to conceive, develop and implement a digital business project.
The course will be divided into three parts. The first part aims to explain the reasons behind the success of digital companies (especially, but not only, startups) and digital innovation dynamics. The second part offers students the technical and methodological tools for the realization of a digital business project. The third part consists in the realization of a project and is characterized by a strongly experimental approach.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810211 -
Algorithms for big data
|
Also available in another semester or year
|
|
|
Optional Group:
I ANNO "DUE A SCELTA TRA "PER ENTRAMBI I CURRICULA - (show)
|
12
|
|
|
|
|
|
|
|
|
20810158 -
Model Identification and Data Analysis
|
Also available in another semester or year
|
|
20810206 -
SYSTEMS IOT FOR LARGE INFRASTRUCTURES
|
Also available in another semester or year
|
|
20801759 -
FUZZY CONTROL
(objectives)
Provide both methodological knowledge and practical skills on fuzzy logic and possibility theory focusing on the realization of process controllers and data processing for sensor data
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801966 -
INDUSTRIAL PRODUCTION MANAGEMENT
|
Also available in another semester or year
|
|
20801761 -
ELEMENTS OF ORGANISATION
(objectives)
Provide the notions and develop the logics necessary to understand the formal description and the actual functioning of firms and institutions, and their evolutionary tendencies related to the evolution of their operating environment. Introducing to organizational analysis, bringing the student to be able to think about the relationships between market, strategy, structure and processes from a total quality perspective and taking into account people's organizational behaviors and motivations.
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
|
Optional Group:
altre attività offerte e cfu a scelta libera dello studente curriculum automazione dei sistemi complessi - (show)
|
12
|
|
|
|
|
|
|
|
|
20810209 -
Complex robotic systems laboratory
(objectives)
Provide students with skills necessary to conceive, develop and complete a complex robotics project. The teaching is characterized by a highly experimental approach and will take place at the Robotics Laboratory of the Department of Engineering.
|
3
|
ING-INF/04
|
27
|
-
|
-
|
-
|
Elective activities
|
ITA |
|
20810210 -
Laboratory di metodi decisionali
(objectives)
Provide students with skills necessary to conceive, develop and complete a complex Operations Research project. The teaching is characterized by a highly experimental approach and will take place at the “Automation and operations research in industry” Laboratory of the Department of Engineering
|
3
|
MAT/09
|
27
|
-
|
-
|
-
|
Elective activities
|
ITA |
|
Second semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20810224 -
MACROEONOMICS
(objectives)
This Course gives students a thorough understanding of macroeconomics by taking a unified view of the subject, allowing connections to be made between the short, medium and long run. By using this unified structure we will analyse the macroeconomic effects of the last financial crisis.
|
9
|
SECS-P/02
|
90
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20810207 -
CONTROL MEASURES AND TECHNOLOGIES
(objectives)
Present the main aspects of the measures and technologies to build modern control systems based on transductors, data extraction and data processing.
To present, in particular, processing of sensory data, estimation techniques for auto and cross-correlation, test signal generation, FFT based harmonic response estimation, as well as the techniques and components at the basis of the actuators of electric engines.
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Optional Group:
I ANNO AUTOMAZIONE DEI SISTEMI COMPLESSI -UNO A SCELTA - (show)
|
6
|
|
|
|
|
|
|
|
|
20810208 -
Decision Support Systems and Analytics
|
Also available in another semester or year
|
|
20802073 -
Optimization of Public Services
(objectives)
This course gives a compendium of techniques, methods and solution
approaches to support the decision making process in the public sector. The
course is based on case studies concerning the design and the management of
public services and it is focused on the development of optimization models
and solution algorithms. Ethical and political issues, typical of the public
sector, are also addressed
|
6
|
MAT/09
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
|
Optional Group:
I ANNO "DUE A SCELTA TRA "PER ENTRAMBI I CURRICULA - (show)
|
12
|
|
|
|
|
|
|
|
|
20810158 -
Model Identification and Data Analysis
(objectives)
Introduce the student to the fundamentals of system identification applied to sampled systems (ARX and ARMAX model, ordinary least squares, recursive least squares, bayesian filtering). Introduce the student to sensor fusion. To familiarize the student with the use of the MatLab identification toolbox
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810206 -
SYSTEMS IOT FOR LARGE INFRASTRUCTURES
(objectives)
The objectives will be the study and comprehension of systems of system theory and the modeling of large infrastructures (distribution networks, telecommunication networks, transportation networks). Interdependencies among different infrastructures will be analyzed with the aim of evaluating the distributed risk and designing resilient systems. Service oriented architectures will be studied as well as distributed awareness systems.
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801759 -
FUZZY CONTROL
|
Also available in another semester or year
|
|
20801966 -
INDUSTRIAL PRODUCTION MANAGEMENT
(objectives)
This course is aimed at providing the basic methodological tools required for production planning and control in manufacturing systems. Specific methods used in make to stock, assemble to order, make to order, and engineering to order are analyzed, also discussing the differences between push and pull production systems. The course follows the traditional hierarchical approach including aggregate production and capacity planning, master production scheduling, materials and manufacturing resources requirements planning (MRP and CRP techniques), order release planning and job scheduling. furthermore, techniques for demand forecasting and implementation of just in time lean manufacturing systems are presented. The course also provides tools to estimate the performances of manufacturing systems, i.e. the links between work in process, throughput and cycle time, including variability effects and lot sizing decisions. finally, production planning decisions are put in perspective with strategic decisions, with capacity planning issues and with inventory management problems.
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801761 -
ELEMENTS OF ORGANISATION
|
Also available in another semester or year
|
|
|
Optional Group:
I ANNO "UNO A SCELTA TRA" PER ENTRAMBI I CURRICULA - (show)
|
6
|
|
|
|
|
|
|
|
|
20801758 -
DATABASES I
|
Also available in another semester or year
|
|
20810087 -
MACHINE LEARNING
|
Also available in another semester or year
|
|
20810205 -
Digital entrepreneurship
(objectives)
Provide students with technical and methodological skills
necessary to conceive, develop and implement a digital business project.
The course will be divided into three parts. The first part aims to explain the reasons behind the success of digital companies (especially, but not only, startups) and digital innovation dynamics. The second part offers students the technical and methodological tools for the realization of a digital business project. The third part consists in the realization of a project and is characterized by a strongly experimental approach.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810211 -
Algorithms for big data
(objectives)
In many application contexts huge volumes of data are produced which are
used in the economic-financial, political, social and even institutional
fields. Often the data is stored in huge distributed clouds and is
sometimes generated according to a continuous flow, so large as to make
complete storage unfeasible. In many cases the data pertains to entities
in close relationship with each other and gives rise to massive networks
of connections. Familiar examples for such networks are biological and
social networks, distribution networks, and the Web graph. Furthermore,
the fact that the data is stored in systems managed by third parties
poses integrity problems, which have not been considered in the
classical IT literature in terms of both their type and scale.
This scenario poses unprecedented algorithmic challenges, which are
being considered by a vast audience of researchers. In the last decade,
this effort has produced many innovations on both the methodological and
technological level. This course aims at transferring to the students
some of the most important methodological tools originated from the
research on Big Data algorithms. These methodological tools are
presented within challenging application contexts.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
SECOND YEAR
First semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20802143 -
Robotics
(objectives)
The course encompasses industrial manipulators and mobile platforms(autonomous vehicles) and provides the basis of the kinematic e dynamic modelling and of the motion control and planning. As for mobile platforms, the focus will be on terrestrial ones, but some attention will be given aslo to aerial ones (drones) anda maritime ones. At the end of the coursee the student will be able to partecipate to projects involving the design and the use of robots.
|
9
|
ING-INF/04
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20802136 -
CYBER PHYSICAL SYSTEMS
(objectives)
Building effective CPSs of the future require multi-disciplinary skills. In particular, the confluence of real-time computing, wireless sensor networks, control theory, signal processing and embedded systems are required to create these new systems. This course will cover some basic material from these areas, but focus on advanced research papers related to CPS.
|
6
|
ING-INF/04
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801784 -
LOGISTICS OPTIMISATION
(objectives)
The objective of the course is to endow the students with advanced knowledge for operations planning and scheduling in manufacturing and logistics systems. Topics include deterministic operations research methodology for the design of decision support systems, modeling, algorithms and applications.
|
9
|
MAT/09
|
81
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
Second semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20801786 -
FINAL EXAM
(objectives)
https://ingegneria.uniroma3.it/didattica/tesi-ed-esame-di-laurea/norme-comuni/
https://ingegneria.uniroma3.it/didattica/collegio-informatica/lauree-e-tirocini/laurea-magistrale/
|
26
|
|
-
|
-
|
-
|
-
|
Final examination and foreign language test
|
ITA |
|
20801785 -
SKILLS FOR THE WORLD OF WORK
(objectives)
Frequency seminars mandatory
|
1
|
|
24
|
-
|
-
|
-
|
Other activities
|
ITA |
Università Roma Tre