Degree Course: Computer science and engineering
A.Y. 2021/2022
Conoscenza e capacità di comprensione
Il laureato magistrale in Ingegneria Informatica avra (i) conoscenze e capacita di comprensione che, estendendo e rafforzando quelle acquisite nella formazione di primo livello, consentono di elaborare e applicare idee originali, (ii) competenze avanzate ad ampio spettro nell'area dell'ingegneria informatica e in alcune sue specifiche sotto-aree, (iii) visione interdisciplinare dei problemi e degli strumenti metodologico/applicativi.
Questi obiettivi sono perseguiti attraverso gli insegnamenti erogati in entrambi gli anni di corso, soprattutto quelli di natura formale e metodologica, e sono verificati attraverso gli esami di profitto.
Capacità di applicare conoscenza e comprensione
Il laureato magistrale sara in grado di applicare le conoscenze acquisite alla risoluzione di problemi complessi relativi anche a tematiche nuove o non familiari, inserite in contesti piu ampi (anche interdisciplinari) connessi all'ingegneria informatica.
In tale ambito, il laureato sara in grado di integrare conoscenze e di condurre, sia autonomamente che in gruppi di lavoro, attivita di analisi, progettazione, realizzazione, valutazione e gestione di sistemi di grandi complessita, nonche di formulare giudizi sulla base di informazioni limitate o incomplete.
Questi obiettivi sono perseguiti attraverso gli insegnamenti erogati in entrambi gli anni, soprattutto attraverso quelli piu sperimentali e che danno spazio ad attivita che prevedono lo sviluppo di progetti, anche da svolgere in gruppo.
Sono inoltre perseguiti attraverso le attivita relative alla tesi di laurea magistrale.
La verifica avviene attraverso gli esami di profitto e l'esame di laurea magistrale.
Autonomia di giudizio
Il laureato magistrale in Ingegneria informatica sara in grado di assumere responsabilita decisionali autonome in merito all?analisi, alla progettazione e alla valutazione di soluzioni informatiche, nel loro contesto applicativo, nell?ambito di progetti e sistemi complessi e di grandi dimensioni, nonche di partecipare attivamente a processi decisionali in contesti anche interdisciplinari.
Questa autonomia di giudizio viene perseguita soprattutto attraverso gli insegnamenti che prevedono una componente progettuale e la predisposizione di relazioni su tali attivita progettuali, nonche attraverso le attivita legate alla tesi di laurea magistrale e alla predisposizione di un relativo elaborato scritto.
Questo obiettivo viene verificato attraverso i relativi esami di profitto e la prova finale.
Abilità comunicative
Il laureato magistrale sara in grado di comunicare e interagire efficacemente sulle tematiche di interesse con interlocutori specialisti e non specialisti, anche di alto livello, sia per comprendere e analizzare le loro necessita e i loro interessi specifici, che per prendere e valutare decisioni progettuali, nonche per comunicare e spiegare le proprie decisioni progettuali e le loro conseguenze.
Queste abilita comunicative vengono perseguite attraverso gli esami e attraverso la tesi di laurea magistrale.
In particolare, sono importanti le attivita che prevedono una componente progettuale, da svolgere individualmente oppure in gruppo, nonche la stesura di relazioni per documentare tali attivita progettuali.
E inoltre previsto un corso dedicato alla comunicazione con il mondo del lavoro e alle soft skill.
Questo obiettivo viene verificato attraverso la predisposizione di forme diversificate per gli esami di profitto (prove scritte, prove orali e relazioni di attivita progettuali) e soprattutto attraverso la prova finale (che prevede sia la scrittura dell?elaborato di tesi magistrale che una sua esposizione orale), consentendo di valutare in modo complessivo le capacita di sintesi, comunicazione ed esposizione raggiunte.
Capacità di apprendimento
Il laureato magistrale sara in grado di procedere in maniera autonoma nell?aggiornamento professionale, per rinnovare le proprie conoscenze ed acquisire nuove conoscenze anche in relazione alle continue evoluzioni delle tecnologie e metodologie informatiche.
Queste capacita di apprendimento vengono perseguite in particolare attraverso alcuni insegnamenti che prevedono una componente seminariale, di ricerca bibliografica e di classificazione della letteratura tecnico/scientifica.
Le capacita vengono ulteriormente consolidate attraverso la tesi di laurea magistrale.
Questo obiettivo viene verificato attraverso i relativi esami di profitto e la prova finale.
Requisiti di ammissione
Per poter accedere al Corso di Laurea magistrale in Ingegneria Informatica lo studente deve essere in possesso di una laurea nella classe L-8 Ingegneria Informatica oppure nella classe L-31 Scienze e tecnologie Informatiche.
Alternativamente, se laureato in classi diverse dalla L-8 ed L-31, lo studente deve aver conseguito almeno 24 CFU nei settori scientifico-disciplinari dell'area MAT e almeno 36 CFU complessivi nei settori scientifico-disciplinari ING-INF/05 o INF/01
Inoltre sono ammessi i possessori di altro titolo acquisito all'estero e riconosciuto idoneo.
Si rinvia al regolamento didattico del corso per la disciplina delle modalità di verifica delle personale preparazione.
La verifica della personale preparazione è obbligatoria in ogni caso, e possono accedervi solo gli studenti in possesso dei requisiti curriculari; in particolare, tale possesso non può essere considerato come verifica della personale preparazione.
In base all'analisi del curriculum individuale dello studente sarà eventualmente possibile individuare percorsi, sotto forma di piani di studio individuali all'interno della laurea magistrale, che conducano al conseguimento della laurea con 120 CFU, senza attività formative aggiuntive.
L'accesso alla Laurea Magistrale in Ingegneria Informatica prevede, inoltre, una verifica dell'adeguatezza della preparazione personale degli studenti, le cui modalita sono definite nel regolamento didattico.
Infine, il possesso di competenze nella lingua inglese che consenta ai laureati di utilizzare fluentemente, in forma scritta e orale, almeno una lingua dell'Unione Europea oltre l'italiano, con riferimento anche ai lessici disciplinari, come richiesto dalla classe di laurea, viene verificato richiedendo in accesso un livello equivalente al B2 del QCER secondo le modalità indicate nel regolamento didattico del corso.
Prova finale
La prova finale e costituita dalla discussione di una tesi di laurea magistrale originale, elaborata in modo autonomo dal candidato.
In particolare, la tesi deve essere relativa ad una significativa attivita nell?ambito dell?Ingegneria Informatica, svolta dal candidato presso l?Universita oppure presso un?azienda o un ente, sotto la guida di un relatore ed eventualmente di uno o piu co-relatori, in cui e normalmente richiesta l?applicazione delle conoscenze e delle capacita apprese nei corsi di insegnamento, spesso con l'integrazione di conoscenze aggiuntive e la formulazione di proposte innovative.
Orientamento in ingresso
Le azioni di orientamento in ingresso sono improntate alla realizzazione di processi di raccordo con la scuola media secondaria.
Si concretizzano sia in attivita informative e di approfondimento dei caratteri formativi dei Corsi di Studio (CdS) dell'Ateneo, sia in un impegno condiviso da scuola e universita 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 attivita promosse si articolano in:
a) incontri e manifestazioni rivolte alle future matricole;
b) sviluppo di servizi online e pubblicazione di guide sull'offerta formativa dei CdS.
L'attivita di orientamento in ingresso prevede quattro principali attivita, distribuite nel corso dell'anno accademico, alle quali partecipano tutti i Dipartimenti e i CdS:
1) 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 realta 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 attivita 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;
2) Autorientamento, un progetto sviluppato in collaborazione diretta con alcune scuole medie superiori per lo sviluppo di una maggiore consapevolezza nella scelta da parte degli studenti.
Il progetto, infatti, e articolato in incontri svolti presso le scuole ed e finalizzato a sollecitare nelle future matricole una riflessione sui propri punti di forza e sui criteri di scelta;
3) Attivita di orientamento sviluppate dai singoli Dipartimenti, mediante incontri in presenza e servizi online;
4) Orientarsi a Roma Tre, rappresenta la manifestazione che riassume le annuali attivita di orientamento in ingresso e si svolge in Ateneo a luglio di ogni anno.
L'evento accoglie, perlopiu, 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 di orientamento online messi a disposizione dei futuri studenti universitari sono nel tempo aumentati, tenendo conto dello sviluppo delle nuove opportunita di comunicazione tramite web.
Inoltre, durante tutte le manifestazioni di presentazione dell'offerta formativa, sono illustrati quei siti web di Dipartimento, di Ateneo, Portale dello studente, etc., che possono aiutare gli studenti nella loro scelta.
Infine, l'Ateneo valuta, di volta in volta, l'opportunita di partecipare ad ulteriori occasioni di orientamento in presenza ovvero online (Salone dello studente ed altre iniziative).
Il Corso di Studio in breve
Il Corso di Laurea Magistrale in Ingegneria Informatica, afferente al Dipartimento di Ingegneria dell'Universita degli Studi Roma Tre e appartenente alla classe delle lauree magistrali LM-32 in Ingegneria Informatica, e finalizzato al conseguimento del titolo di studio universitario: Laurea Magistrale in Ingegneria Informatica.
Il corso di laurea magistrale mira a formare laureati con solide basi metodologiche e con una elevata qualificazione professionale nell'area dell'Ingegneria dell'Informazione, che siano in grado di operare efficacemente nei numerosi settori applicativi che ne richiedono le competenze, di identificare, formulare e risolvere problemi complessi e/o che richiedano approcci e soluzioni originali, di promuovere e gestire l'innovazione tecnologica, di adeguarsi ai rapidi mutamenti tipici dei settori ad alta tecnologia.
In particolare, l'obiettivo e quello di fornire le basi culturali e le capacita tecniche e operative necessarie per progettare sistemi di elevata complessita nell'ambito dei sistemi informativi e di calcolo ad alte prestazioni, dei sistemi software distribuiti e orientati a Internet e delle reti di comunicazione.
Il corso di studio e ad accesso libero, senza numero programmato, ed il requisito richiesto e il possesso di una laurea nella Classe delle Lauree in Ingegneria dell'Informazione o nella Classe delle Lauree in Scienze e Tecnologie Informatiche.
Inoltre, e necessario che lo studente abbia competenze di: analisi matematica, geometria ed algebra, fisica, ricerca operativa, fondamenti di informatica, algoritmi e strutture di dati, calcolatori elettronici, basi di dati, economia applicata all'Ingegneria, reti di calcolatori e programmazione orientata agli oggetti tipiche dei corsi di laurea in Ingegneria Informatica.
Pertanto, per accedere al corso di studio e necessario presentare una domanda di pre-iscrizione, documentando tutte le attivita formative del proprio piano di studio relativo alla Laurea.
Il corso di studi e organizzato con (i) un primo anno dedicato al consolidamento e al rafforzamento della formazione ingegneristica di primo livello, tanto nei settori caratterizzanti dell'informatica quanto nei settori delle discipline affini e integrative e (ii) un secondo anno, dedicato all'acquisizione di conoscenze avanzate e d'avanguardia nei settori caratterizzanti dell'informatica, conseguite anche attraverso importanti attivita di progettazione e/o di ricerca.
Il percorso previsto contempera la formazione di base, garantita da una serie di insegnamenti di ampio respiro, con elementi di natura professionalizzante avanzata, che sono sviluppati in insegnamenti di valenza applicativa.
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 e orientato alla formazione di tecnici aventi le competenze richieste per operare in numerose realta lavorative, incluse le industrie informatiche operanti negli ambiti della produzione software, dalle aziende dei settori dei sistemi informativi, delle reti di calcolatori e delle telecomunicazioni, dalle strutture competenti per l'informatica nelle pubbliche amministrazioni, nelle imprese di servizi e, nel caso degli studenti migliori, nella ricerca scientifica.
Il percorso di studi e 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 attivita formative presso istituzioni universitarie estere, ad esempio tramite programmi Erasmus o attraverso lo svolgimento del lavoro di tesi presso aziende, universita 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.
Sistemi Informatici Complessi
FIRST YEAR
First semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20810256 -
Automata, Languages and Computing
(objectives)
Introduce the students to the theory of languages and, at the same time, to the theory of automata. introduce computability and complexity paradigms. At the end of the course students should know new formal methodologies, should be able to critically review, from the perspective of their expressive potential, already known methodologies and should be able to classify problems from the point of view of the resources required for their solution.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
Optional Group:
curriculum Sistemi Informatici Complessi I ANNO QUATTRO A SCELTA - (show)
|
36
|
|
|
|
|
|
|
|
|
20801730 -
ARTIFICIAL INTELLIGENCE
(objectives)
The goal is to present the fundamental models, methods and techniques of various areas of Artificial Intelligence, with particular reference to heuristic search, knowledge representation and automatic reasoning, machine learning, natural language processing, computer vision. The lessons and practical exercises carried out during the course will allow the student to acquire analytical and problem solving skills on various domains of interest for the discipline.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810007 -
ARCHITETTURA DEI SISTEMI SOFTWARE
|
Also available in another semester or year
|
|
20810157 -
PARALLEL AND DISTRIBUTED COMPUTING
|
Also available in another semester or year
|
|
20810259 -
Internet and Data Centers
(objectives)
The purpose is to provide advanced knowledge on computer networks and data centers, with methodological and technical contents. Special attention is devoted to scalability issues. At the end of the course the student is supposed to get the following concepts: inter-domain and intra-domain routing, congestion control, architectures for scalable systems. The student is also supposed to get advanced technicalities on widely adopted protocols. Finally, the student is supposed to understand the main economic and technical drivers of the internet and data centers evolution.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810260 -
Tecnologie e Architetture per la Gestione dei Dati
|
Also available in another semester or year
|
|
20810266 -
Machine Learning
|
Also available in another semester or year
|
|
|
Optional Group:
curriculum Sistemi Informatici Complessi - curriculum ingegneria dei Dati- curriculum Algoritmi, Big Data e Machine Learning-I ANNO DUE A SCELTA - (show)
|
12
|
|
|
|
|
|
|
|
|
20810252 -
MODELS AND ALGORITHMS FOR OPTIMIZATION
(objectives)
The course aims at providing basic methodological and operative knowledge to represent and cope with decision processes and quantitative models.
|
6
|
MAT/09
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
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 |
|
20810258 -
New Generation Mobile Networks
(objectives)
To acquire general concept on new generation mobile networks (3G, 4G, 5G, 6G) as part of a communication system. To provide an overview on main operating principles of a structured mobile network, such as the available services also from a financial and economic viewpoint, quality requirements, mobility management, security, secrecy and authentication problems, localization services, power control of connected devices, access technologies from wireless devices, evolution of architecture of SW network virtualization, algorithms of array processing to allow dedicated efficient links in modern standards (5G and beyond) between terminals or smart objects connected to the IoT world.
|
6
|
ING-INF/03
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20810257 -
Diritto dei Dati
|
Also available in another semester or year
|
|
20801648 -
PROBABILITY AND STATISTICS
|
Also available in another semester or year
|
|
|
Optional Group:
"12 CFU a scelta dello studente" per il curriculum Sistemi Informatici complessi - (show)
|
12
|
|
|
|
|
|
|
|
|
20810265 -
Next Generation Computing Models
(objectives)
Introduce the complexity classes related to the most recent computing paradigms. At the end of the course the students will be able to compare the computational power of the new computing paradigms with the computational power of the more established ones.
|
3
|
ING-INF/05
|
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
|
|
Optional Group:
curriculum Sistemi Informatici Complessi I ANNO QUATTRO A SCELTA - (show)
|
36
|
|
|
|
|
|
|
|
|
20801730 -
ARTIFICIAL INTELLIGENCE
|
Also available in another semester or year
|
|
20810007 -
ARCHITETTURA DEI SISTEMI SOFTWARE
(objectives)
This unit presents software systems architecture, and it involves both methodological and technological issues. Software architecture has a fundamental role in achieving the quality (i.e., non functional) properties of software systems. In particular, the unit will study the architecture of distributed software systems, including the component-based architecture, the service-oriented architecture, and architectures for the Cloud
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810157 -
PARALLEL AND DISTRIBUTED COMPUTING
(objectives)
The course aims to develop the skill needed to produce computer programs for parallel and distributed computation. The theory is carefully linked to practice by implementing programming projects in a cutting edge environment
|
9
|
ING-INF/05
|
72
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810259 -
Internet and Data Centers
|
Also available in another semester or year
|
|
20810260 -
Tecnologie e Architetture per la Gestione dei Dati
(objectives)
The goal of the course is to present models, methods and systems that play a fundamental role in database technology, together with discussions on the recent evolution of the technology itself. The directions of development to be considered include integration of heterogeneous and autonomous systems; databases for business intelligence and decision support. After taking the course, the student will know the major features of relational database technology, the methods for data integration, and for the design of data warehouses.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810266 -
Machine Learning
(objectives)
The course will allow students to deepen the methods and algorithms typical of Machine Learning (supervised, unsupervised and with reinforcement) and to use them as tools for the development of innovative technologies. In particular, aspects of the main areas of the discipline will be studied, including regression, classification and clustering. The methods and techniques of deep learning and specialized development environments will then be introduced. The course includes the development of an individual or group project that will allow students to apply the theoretical foundations learned in class to concrete problems on various domains of interest. They will be related, for example, to how to analyze large and complex datasets in various fields (e.g., Health Care, Data Science, Data Mining, Financial Analysis, Videogames, Computer Vision, etc.), create systems that adapt and improve over time (e.g., Recommender Systems), and so on. Finally, the course includes monographic seminars dedicated to various case studies.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
|
Optional Group:
curriculum Sistemi Informatici Complessi - curriculum ingegneria dei Dati- curriculum Algoritmi, Big Data e Machine Learning-I ANNO DUE A SCELTA - (show)
|
12
|
|
|
|
|
|
|
|
|
20810252 -
MODELS AND ALGORITHMS FOR OPTIMIZATION
|
Also available in another semester or year
|
|
20810208 -
Decision Support Systems and Analytics
|
Also available in another semester or year
|
|
20810258 -
New Generation Mobile Networks
|
Also available in another semester or year
|
|
20810257 -
Diritto dei Dati
(objectives)
Provide an introduction to the main principles and rules concerning data governance under Italian and European law. Study the legal distinction between personal and non personal data, with reference to the main instruments related to property, access, and circulation of data (intellectual property, trade secret, personal data protection). Analyse the issues deriving from the use of data in algorithmic decisions, both in administrative and private law contexts.
|
6
|
IUS/02
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20801648 -
PROBABILITY AND STATISTICS
(objectives)
To provide the fundamental elements of probability theory and mathematical statistics, along with some tools of parametric statistics, which may be useful in practice.
|
6
|
MAT/06
|
54
|
-
|
-
|
-
|
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
|
|
Optional Group:
curriculum Sistemi Informatici Complessi II ANNO -QUATTRO A SCELTA TRA - (show)
|
24
|
|
|
|
|
|
|
|
|
20801798 -
INTELLIGENT SYSTEMS FOR THE INTERNET
(objectives)
The course will allow students to learn various methods for the design, implementation, and testing of adaptive systems on the Web, created through Artificial Intelligence techniques, with particular reference to Machine Learning techniques. Specific attention will be paid to Information Retrieval systems, such as search engines, crawlers and document feeds. Classic retrieval models will be studied, such as the Vector Space Model and probabilistic models, document ranking techniques, as well as the PageRank algorithm used by Google. Machine Learning methods in Information Retrieval will be addressed, including techniques for Sentiment Analysis, User Modeling methods necessary for personalized search, and social search applications involving communities of individuals in activities such as content tagging and question answering. The techniques for analyzing social networks (e.g., Facebook and Twitter) will be explored, which will allow us to explore phenomena such as the spread of fake news, the filter bubble, and the polarization of users. Finally, Recommender Systems will be studied, from basic algorithms (e.g., collaborative filtering) to application scenarios (e.g., movies, books, music artists and songs).
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20802125 -
BIG DATA
|
Also available in another semester or year
|
|
20810006 -
ADVANCED TOPICS IN COMPUTER SCIENCE
|
Also available in another semester or year
|
|
20810140 -
CYBERSECURITY
(objectives)
The Cybersecurity course intends to provide the student with competencies needed for understanding and tackling cybersecurity problems for ICT systems and complex organizations, to design networks and computing systems with a certain level of security, and to planning e manage activities related to cybersecurity. The course provides competences about attacks, countermeasures, cryptographic tools, applications, and methodologies in the cybersecurity field. Advanced topics in data integrity are also addressed.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20802126 -
INFORMATION VISUALIZATION
|
Also available in another semester or year
|
|
20810205 -
Digital entrepreneurship
|
Also available in another semester or year
|
|
20810211 -
Algorithms for big data
|
Also available in another semester or year
|
|
20810223 -
INGEGNERIA DEI DATI
(objectives)
Providing skills on systems, methods, and technologies for extraction, cleaning, analyzing and integrating and management of unstructured data.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810261 -
Computer Graphics
(objectives)
This course aims at illustrating the modern software and hardware computer graphics architectures, and at providing mathematical, technical and methodological solutions for the development of projects concerning the visualization of data in 2D or 3D. The course will expose base concepts in computer graphics such as spaces, curves, surfaces and volumes, focusing on notions and algorithms currently used in scientific visualization, videogames, and computer animation. Moreover, this course aims at exposing details of hardware and software platforms currently in use.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20802136 -
CYBER PHYSICAL SYSTEMS
(objectives)
Building effective CPS 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 |
|
20810262 -
Deep Learning
(objectives)
Provide advanced and specific skills in Deep neural networks. The course consists of a theoretical part on the fundamental concepts, and laboratory activities in which these concepts are applied and developed through a software framework. At the end of the course the student will be able to: adequately train and optimize Deep neural networks; distinguish between different solutions and be able to choose and customize the most effective architectures in real-world scenarios, supervised, unsupervised or following a reinforcement learning approach.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810263 -
Logica
|
Also available in another semester or year
|
|
20810264 -
Pianificazione Automatica
(objectives)
The course presents Artificial Intelligence planning problems. It introduces models and resolution techniques for both "classic" and temporal planning, involving scheduling aspects. Different methodologies for the synthesis of action plans and their execution will be presented, as well as aspects related to automated learning of classical planning domains. Furthermore, some applications and samples will be presented and discussed, also in relation to the control of autonomous robots
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Second semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20801785 -
SKILLS FOR THE WORLD OF WORK
(objectives)
The course aims to present the main soft skills for employment access through seminars held by speakers from the production reality. The seminaris illustrate the job and career dynamics in different types of companies (startups, SMEs, multinationals) in different sectors (software integrators, service companies, product companies, insurance and banking groups, utilities). Soft skills include how to write an effective CV, how to address the job interview. The course also introduces basic notions of labor laws.
|
1
|
|
24
|
-
|
-
|
-
|
Other activities
|
ITA |
|
Optional Group:
curriculum Sistemi Informatici Complessi II ANNO -QUATTRO A SCELTA TRA - (show)
|
24
|
|
|
|
|
|
|
|
|
20801798 -
INTELLIGENT SYSTEMS FOR THE INTERNET
|
Also available in another semester or year
|
|
20802125 -
BIG DATA
(objectives)
The goal of the course is to illustrate the modern solutions to the management of big data, very large repositories of de-structured data. Starting from the requirements of modern database applications, the course will illustrate the hardware and software architectures that have been recently proposed for the management and analysis of big data. The topics addressed in the course will include: cluster architectures, map-reduce paradigm, cloud computing, NoSQL systems, tools and languages for data analysis. Both theoretical and practical aspects will be addressed and the discussed technologies will be experimented during practical classes and through the assignment of projects.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810006 -
ADVANCED TOPICS IN COMPUTER SCIENCE
(objectives)
The goal of the course is to present models, methods and systems related to the latest advances in the field of information technology able to meet the requirements of modern applications. The course is taught in English by foreign professors of high qualification
|
6
|
ING-INF/05
|
42
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810140 -
CYBERSECURITY
|
Also available in another semester or year
|
|
20802126 -
INFORMATION VISUALIZATION
(objectives)
The goal of this course is that of introducing the participants to the problems and the solutions in the area of the visual exploration of abstract data, with a particular emphasis on the visual perception phenomena, on the graphic metaphors that can be exploited and on thealgorithmic methods and models that can be adopted. The
knowledge of the participants about algorithm engineering and network optimization problems will be deepened. Such a knowledge will be applied to different strains of visualization problems with a strong practical approach.
|
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
(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 |
|
20810223 -
INGEGNERIA DEI DATI
|
Also available in another semester or year
|
|
20810261 -
Computer Graphics
|
Also available in another semester or year
|
|
20802136 -
CYBER PHYSICAL SYSTEMS
|
Also available in another semester or year
|
|
20810262 -
Deep Learning
|
Also available in another semester or year
|
|
20810263 -
Logica
(objectives)
The course aims at giving basic knowledge of classical and some non-classical logics, their formal semantics and proof systems. Students will acquire the capability to use the studied logics for representation purposes and will be presented with some important applications of logic in computer science.
|
6
|
ING-INF/05
|
60
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810264 -
Pianificazione Automatica
|
Also available in another semester or year
|
|
|
20802019 -
FINAL EXAM
(objectives)
https://ingegneria.uniroma3.it/didattica/collegio-informatica/lauree-e-tirocini/laurea-magistrale/
|
26
|
|
650
|
-
|
-
|
-
|
Final examination and foreign language test
|
ITA |
Ingegneria dei Dati
FIRST YEAR
First semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20810256 -
Automata, Languages and Computing
(objectives)
Introduce the students to the theory of languages and, at the same time, to the theory of automata. introduce computability and complexity paradigms. At the end of the course students should know new formal methodologies, should be able to critically review, from the perspective of their expressive potential, already known methodologies and should be able to classify problems from the point of view of the resources required for their solution.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Optional Group:
curriculum Sistemi Informatici Complessi - curriculum ingegneria dei Dati- curriculum Algoritmi, Big Data e Machine Learning-I ANNO DUE A SCELTA - (show)
|
12
|
|
|
|
|
|
|
|
|
20810252 -
MODELS AND ALGORITHMS FOR OPTIMIZATION
(objectives)
The course aims at providing basic methodological and operative knowledge to represent and cope with decision processes and quantitative models.
|
6
|
MAT/09
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
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 |
|
20810258 -
New Generation Mobile Networks
(objectives)
To acquire general concept on new generation mobile networks (3G, 4G, 5G, 6G) as part of a communication system. To provide an overview on main operating principles of a structured mobile network, such as the available services also from a financial and economic viewpoint, quality requirements, mobility management, security, secrecy and authentication problems, localization services, power control of connected devices, access technologies from wireless devices, evolution of architecture of SW network virtualization, algorithms of array processing to allow dedicated efficient links in modern standards (5G and beyond) between terminals or smart objects connected to the IoT world.
|
6
|
ING-INF/03
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20810257 -
Diritto dei Dati
|
Also available in another semester or year
|
|
20801648 -
PROBABILITY AND STATISTICS
|
Also available in another semester or year
|
|
|
Optional Group:
curriculum Ingegneria dei dati - I anno- tre a scelta tra - (show)
|
27
|
|
|
|
|
|
|
|
|
20810007 -
Software Systems Architecture
|
Also available in another semester or year
|
|
20810157 -
PARALLEL AND DISTRIBUTED COMPUTING
|
Also available in another semester or year
|
|
20801730 -
ARTIFICIAL INTELLIGENCE
(objectives)
The goal is to present the fundamental models, methods and techniques of various areas of Artificial Intelligence, with particular reference to heuristic search, knowledge representation and automatic reasoning, machine learning, natural language processing, computer vision. The lessons and practical exercises carried out during the course will allow the student to acquire analytical and problem solving skills on various domains of interest for the discipline.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810259 -
Internet and Data Centers
(objectives)
The purpose is to provide advanced knowledge on computer networks and data centers, with methodological and technical contents. Special attention is devoted to scalability issues. At the end of the course the student is supposed to get the following concepts: inter-domain and intra-domain routing, congestion control, architectures for scalable systems. The student is also supposed to get advanced technicalities on widely adopted protocols. Finally, the student is supposed to understand the main economic and technical drivers of the internet and data centers evolution.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810266 -
Machine Learning
|
Also available in another semester or year
|
|
Second semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20810260 -
Tecnologie e Architetture per la Gestione dei Dati
(objectives)
The goal of the course is to present models, methods and systems that play a fundamental role in database technology, together with discussions on the recent evolution of the technology itself. The directions of development to be considered include integration of heterogeneous and autonomous systems; databases for business intelligence and decision support. After taking the course, the student will know the major features of relational database technology, the methods for data integration, and for the design of data warehouses.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Optional Group:
curriculum Sistemi Informatici Complessi - curriculum ingegneria dei Dati- curriculum Algoritmi, Big Data e Machine Learning-I ANNO DUE A SCELTA - (show)
|
12
|
|
|
|
|
|
|
|
|
20810252 -
MODELS AND ALGORITHMS FOR OPTIMIZATION
|
Also available in another semester or year
|
|
20810208 -
Decision Support Systems and Analytics
|
Also available in another semester or year
|
|
20810258 -
New Generation Mobile Networks
|
Also available in another semester or year
|
|
20810257 -
Diritto dei Dati
(objectives)
Provide an introduction to the main principles and rules concerning data governance under Italian and European law. Study the legal distinction between personal and non personal data, with reference to the main instruments related to property, access, and circulation of data (intellectual property, trade secret, personal data protection). Analyse the issues deriving from the use of data in algorithmic decisions, both in administrative and private law contexts.
|
6
|
IUS/02
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20801648 -
PROBABILITY AND STATISTICS
(objectives)
To provide the fundamental elements of probability theory and mathematical statistics, along with some tools of parametric statistics, which may be useful in practice.
|
6
|
MAT/06
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
|
Optional Group:
curriculum Ingegneria dei dati - I anno- tre a scelta tra - (show)
|
27
|
|
|
|
|
|
|
|
|
20810007 -
Software Systems Architecture
(objectives)
This unit presents software systems architecture, and it involves both methodological and technological issues. Software architecture has a fundamental role in achieving the quality (i.e., non functional) properties of software systems. In particular, the unit will study the architecture of distributed software systems, including the component-based architecture, the service-oriented architecture, and architectures for the Cloud
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810157 -
PARALLEL AND DISTRIBUTED COMPUTING
(objectives)
The course aims to develop the skill needed to produce computer programs for parallel and distributed computation. The theory is carefully linked to practice by implementing programming projects in a cutting edge environment
|
9
|
ING-INF/05
|
72
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801730 -
ARTIFICIAL INTELLIGENCE
|
Also available in another semester or year
|
|
20810259 -
Internet and Data Centers
|
Also available in another semester or year
|
|
20810266 -
Machine Learning
(objectives)
The course will allow students to deepen the methods and algorithms typical of Machine Learning (supervised, unsupervised and with reinforcement) and to use them as tools for the development of innovative technologies. In particular, aspects of the main areas of the discipline will be studied, including regression, classification and clustering. The methods and techniques of deep learning and specialized development environments will then be introduced. The course includes the development of an individual or group project that will allow students to apply the theoretical foundations learned in class to concrete problems on various domains of interest. They will be related, for example, to how to analyze large and complex datasets in various fields (e.g., Health Care, Data Science, Data Mining, Financial Analysis, Videogames, Computer Vision, etc.), create systems that adapt and improve over time (e.g., Recommender Systems), and so on. Finally, the course includes monographic seminars dedicated to various case studies.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
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
|
|
Optional Group:
Curriculum Ingegneria dei dati - II anno due a scelta tra - (show)
|
12
|
|
|
|
|
|
|
|
|
Second semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20801785 -
SKILLS FOR THE WORLD OF WORK
(objectives)
The course aims to present the main soft skills for employment access through seminars held by speakers from the production reality. The seminaris illustrate the job and career dynamics in different types of companies (startups, SMEs, multinationals) in different sectors (software integrators, service companies, product companies, insurance and banking groups, utilities). Soft skills include how to write an effective CV, how to address the job interview. The course also introduces basic notions of labor laws.
|
1
|
|
24
|
-
|
-
|
-
|
Other activities
|
ITA |
|
20802019 -
FINAL EXAM
(objectives)
https://ingegneria.uniroma3.it/didattica/collegio-informatica/lauree-e-tirocini/laurea-magistrale/
|
26
|
|
650
|
-
|
-
|
-
|
Final examination and foreign language test
|
ITA |
|
Optional Group:
Curriculum Ingegneria dei dati - II anno due a scelta tra - (show)
|
12
|
|
|
|
|
|
|
|
|
20810006 -
ADVANCED TOPICS IN COMPUTER SCIENCE
(objectives)
The goal of the course is to present models, methods and systems related to the latest advances in the field of information technology able to meet the requirements of modern applications. The course is taught in English by foreign professors of high qualification
|
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 |
|
20810140 -
CYBERSECURITY
|
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 |
|
20810263 -
Logica
(objectives)
The course aims at giving basic knowledge of classical and some non-classical logics, their formal semantics and proof systems. Students will acquire the capability to use the studied logics for representation purposes and will be presented with some important applications of logic in computer science.
|
6
|
ING-INF/05
|
60
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20802126 -
INFORMATION VISUALIZATION
(objectives)
The goal of this course is that of introducing the participants to the problems and the solutions in the area of the visual exploration of abstract data, with a particular emphasis on the visual perception phenomena, on the graphic metaphors that can be exploited and on thealgorithmic methods and models that can be adopted. The
knowledge of the participants about algorithm engineering and network optimization problems will be deepened. Such a knowledge will be applied to different strains of visualization problems with a strong practical approach.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
|
20802125 -
BIG DATA
(objectives)
The goal of the course is to illustrate the modern solutions to the management of big data, very large repositories of de-structured data. Starting from the requirements of modern database applications, the course will illustrate the hardware and software architectures that have been recently proposed for the management and analysis of big data. The topics addressed in the course will include: cluster architectures, map-reduce paradigm, cloud computing, NoSQL systems, tools and languages for data analysis. Both theoretical and practical aspects will be addressed and the discussed technologies will be experimented during practical classes and through the assignment of projects.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810223 -
DATA ENGINEERING
(objectives)
Providing skills on systems, methods, and technologies for extraction, cleaning, analyzing and integrating and management of unstructured data.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810288 -
12 CFU FREE OF THE STUDENT
|
12
|
|
108
|
-
|
-
|
-
|
Elective activities
|
ITA |
Intelligenza artificiale e Machine Learning
FIRST YEAR
First semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20810256 -
Automata, Languages and Computing
(objectives)
Introduce the students to the theory of languages and, at the same time, to the theory of automata. introduce computability and complexity paradigms. At the end of the course students should know new formal methodologies, should be able to critically review, from the perspective of their expressive potential, already known methodologies and should be able to classify problems from the point of view of the resources required for their solution.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801730 -
ARTIFICIAL INTELLIGENCE
(objectives)
The goal is to present the fundamental models, methods and techniques of various areas of Artificial Intelligence, with particular reference to heuristic search, knowledge representation and automatic reasoning, machine learning, natural language processing, computer vision. The lessons and practical exercises carried out during the course will allow the student to acquire analytical and problem solving skills on various domains of interest for the discipline.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Optional Group:
curriculum Intelligenza Artificiale e Machine Learning- I anno - due a scelta tra - (show)
|
18
|
|
|
|
|
|
|
|
|
Second semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20810263 -
Logica
(objectives)
The course aims at giving basic knowledge of classical and some non-classical logics, their formal semantics and proof systems. Students will acquire the capability to use the studied logics for representation purposes and will be presented with some important applications of logic in computer science.
|
6
|
ING-INF/05
|
60
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810266 -
Machine Learning
(objectives)
The course will allow students to deepen the methods and algorithms typical of Machine Learning (supervised, unsupervised and with reinforcement) and to use them as tools for the development of innovative technologies. In particular, aspects of the main areas of the discipline will be studied, including regression, classification and clustering. The methods and techniques of deep learning and specialized development environments will then be introduced. The course includes the development of an individual or group project that will allow students to apply the theoretical foundations learned in class to concrete problems on various domains of interest. They will be related, for example, to how to analyze large and complex datasets in various fields (e.g., Health Care, Data Science, Data Mining, Financial Analysis, Videogames, Computer Vision, etc.), create systems that adapt and improve over time (e.g., Recommender Systems), and so on. Finally, the course includes monographic seminars dedicated to various case studies.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Optional Group:
curriculum Intelligenza Artificiale e Machine Learning- I anno - due a scelta tra - (show)
|
18
|
|
|
|
|
|
|
|
|
20810007 -
Software Systems Architecture
(objectives)
This unit presents software systems architecture, and it involves both methodological and technological issues. Software architecture has a fundamental role in achieving the quality (i.e., non functional) properties of software systems. In particular, the unit will study the architecture of distributed software systems, including the component-based architecture, the service-oriented architecture, and architectures for the Cloud
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810157 -
PARALLEL AND DISTRIBUTED COMPUTING
(objectives)
The course aims to develop the skill needed to produce computer programs for parallel and distributed computation. The theory is carefully linked to practice by implementing programming projects in a cutting edge environment
|
9
|
ING-INF/05
|
72
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810259 -
Internet and Data Centers
|
Also available in another semester or year
|
|
20810260 -
Tecnologie e Architetture per la Gestione dei Dati
(objectives)
The goal of the course is to present models, methods and systems that play a fundamental role in database technology, together with discussions on the recent evolution of the technology itself. The directions of development to be considered include integration of heterogeneous and autonomous systems; databases for business intelligence and decision support. After taking the course, the student will know the major features of relational database technology, the methods for data integration, and for the design of data warehouses.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
|
20801648 -
PROBABILITY AND STATISTICS
(objectives)
To provide the fundamental elements of probability theory and mathematical statistics, along with some tools of parametric statistics, which may be useful in practice.
|
6
|
MAT/06
|
54
|
-
|
-
|
-
|
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
|
|
20801798 -
INTELLIGENT SYSTEMS FOR THE INTERNET
(objectives)
The course will allow students to learn various methods for the design, implementation, and testing of adaptive systems on the Web, created through Artificial Intelligence techniques, with particular reference to Machine Learning techniques. Specific attention will be paid to Information Retrieval systems, such as search engines, crawlers and document feeds. Classic retrieval models will be studied, such as the Vector Space Model and probabilistic models, document ranking techniques, as well as the PageRank algorithm used by Google. Machine Learning methods in Information Retrieval will be addressed, including techniques for Sentiment Analysis, User Modeling methods necessary for personalized search, and social search applications involving communities of individuals in activities such as content tagging and question answering. The techniques for analyzing social networks (e.g., Facebook and Twitter) will be explored, which will allow us to explore phenomena such as the spread of fake news, the filter bubble, and the polarization of users. Finally, Recommender Systems will be studied, from basic algorithms (e.g., collaborative filtering) to application scenarios (e.g., movies, books, music artists and songs).
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Optional Group:
curriculum Intelligenza Artificiale e Machine Learning- II anno - due a scelta tra - (show)
|
12
|
|
|
|
|
|
|
|
|
20810006 -
ADVANCED TOPICS IN COMPUTER SCIENCE
|
Also available in another semester or year
|
|
20802125 -
BIG DATA
|
Also available in another semester or year
|
|
20802136 -
CYBER PHYSICAL SYSTEMS
(objectives)
Building effective CPS 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 |
|
20810262 -
Deep Learning
(objectives)
Provide advanced and specific skills in Deep neural networks. The course consists of a theoretical part on the fundamental concepts, and laboratory activities in which these concepts are applied and developed through a software framework. At the end of the course the student will be able to: adequately train and optimize Deep neural networks; distinguish between different solutions and be able to choose and customize the most effective architectures in real-world scenarios, supervised, unsupervised or following a reinforcement learning approach.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810205 -
Digital entrepreneurship
|
Also available in another semester or year
|
|
20810223 -
DATA ENGINEERING
(objectives)
Providing skills on systems, methods, and technologies for extraction, cleaning, analyzing and integrating and management of unstructured data.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810264 -
Pianificazione Automatica
(objectives)
The course presents Artificial Intelligence planning problems. It introduces models and resolution techniques for both "classic" and temporal planning, involving scheduling aspects. Different methodologies for the synthesis of action plans and their execution will be presented, as well as aspects related to automated learning of classical planning domains. Furthermore, some applications and samples will be presented and discussed, also in relation to the control of autonomous robots
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810140 -
CYBERSECURITY
(objectives)
The Cybersecurity course intends to provide the student with competencies needed for understanding and tackling cybersecurity problems for ICT systems and complex organizations, to design networks and computing systems with a certain level of security, and to planning e manage activities related to cybersecurity. The course provides competences about attacks, countermeasures, cryptographic tools, applications, and methodologies in the cybersecurity field. Advanced topics in data integrity are also addressed.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
|
Optional Group:
curriculum Intelligenza Artificiale e Machine Learning- II anno - uno a scelta tra - (show)
|
6
|
|
|
|
|
|
|
|
|
20810252 -
MODELS AND ALGORITHMS FOR OPTIMIZATION
(objectives)
The course aims at providing basic methodological and operative knowledge to represent and cope with decision processes and quantitative models.
|
6
|
MAT/09
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20810267 -
Artificial Intelligence from Engineering to Arts
|
Also available in another semester or year
|
|
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 |
|
20810254 -
TEORIA DEI GIOCHI
(objectives)
The aim of the course is the acquisition of formal tools to model strategic interactions between two or more players, typically rational individuals who make decisions in order to optimize their subjective goals. During the course, cooperative and non-cooperative games will be studied, starting from applications in the social, political or economic fields, to arrive at applications in various fields of artificial intelligence, from the training of neural networks to reinforcement learning in multi-agent systems.
|
6
|
MAT/09
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20802061 -
MULTIMEDIA LABORATORY
|
Also available in another semester or year
|
|
20810258 -
New Generation Mobile Networks
(objectives)
To acquire general concept on new generation mobile networks (3G, 4G, 5G, 6G) as part of a communication system. To provide an overview on main operating principles of a structured mobile network, such as the available services also from a financial and economic viewpoint, quality requirements, mobility management, security, secrecy and authentication problems, localization services, power control of connected devices, access technologies from wireless devices, evolution of architecture of SW network virtualization, algorithms of array processing to allow dedicated efficient links in modern standards (5G and beyond) between terminals or smart objects connected to the IoT world.
|
6
|
ING-INF/03
|
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
|
|
20801785 -
SKILLS FOR THE WORLD OF WORK
(objectives)
The course aims to present the main soft skills for employment access through seminars held by speakers from the production reality. The seminaris illustrate the job and career dynamics in different types of companies (startups, SMEs, multinationals) in different sectors (software integrators, service companies, product companies, insurance and banking groups, utilities). Soft skills include how to write an effective CV, how to address the job interview. The course also introduces basic notions of labor laws.
|
1
|
|
24
|
-
|
-
|
-
|
Other activities
|
ITA |
|
20802019 -
FINAL EXAM
(objectives)
https://ingegneria.uniroma3.it/didattica/collegio-informatica/lauree-e-tirocini/laurea-magistrale/
|
26
|
|
650
|
-
|
-
|
-
|
Final examination and foreign language test
|
ITA |
|
Optional Group:
curriculum Intelligenza Artificiale e Machine Learning- II anno - due a scelta tra - (show)
|
12
|
|
|
|
|
|
|
|
|
20810006 -
ADVANCED TOPICS IN COMPUTER SCIENCE
(objectives)
The goal of the course is to present models, methods and systems related to the latest advances in the field of information technology able to meet the requirements of modern applications. The course is taught in English by foreign professors of high qualification
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20802125 -
BIG DATA
(objectives)
The goal of the course is to illustrate the modern solutions to the management of big data, very large repositories of de-structured data. Starting from the requirements of modern database applications, the course will illustrate the hardware and software architectures that have been recently proposed for the management and analysis of big data. The topics addressed in the course will include: cluster architectures, map-reduce paradigm, cloud computing, NoSQL systems, tools and languages for data analysis. Both theoretical and practical aspects will be addressed and the discussed technologies will be experimented during practical classes and through the assignment of projects.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20802136 -
CYBER PHYSICAL SYSTEMS
|
Also available in another semester or year
|
|
20810262 -
Deep 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 |
|
20810223 -
DATA ENGINEERING
|
Also available in another semester or year
|
|
20810264 -
Pianificazione Automatica
|
Also available in another semester or year
|
|
20810140 -
CYBERSECURITY
|
Also available in another semester or year
|
|
|
Optional Group:
curriculum Intelligenza Artificiale e Machine Learning- II anno - uno a scelta tra - (show)
|
6
|
|
|
|
|
|
|
|
|
20810252 -
MODELS AND ALGORITHMS FOR OPTIMIZATION
|
Also available in another semester or year
|
|
20810267 -
Artificial Intelligence from Engineering to Arts
(objectives)
The educational objective of the present course is to bring the student closer to some applications of Artificial Intelligence (AI) and Machine Learning (ML) in the engineering and artistic fields. The course is therefore designed in two parts: the first concerns AI applications to electrical energy and information engineering; the second focuses on the use of ML techniques for musical and artistic production in general. Thus, the student will have the opportunity to learn how AI is a very versatile and performing tool in application fields that are very distant culturally.
|
6
|
ING-IND/31
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20810208 -
Decision Support Systems and Analytics
|
Also available in another semester or year
|
|
20810254 -
TEORIA DEI GIOCHI
|
Also available in another semester or year
|
|
20802061 -
MULTIMEDIA LABORATORY
(objectives)
The course aims at illustrating the more recent techniques for multimedia signal processing. Video signals and images will be analyzed in both bi-dimensional and tri-dimensional case. The course will be organized in two parts: in the first, the basics needed for multimedia signal processing and programming in Matlab will be presented to the students. In the second part practical experiences will be performed, both in individual and in group assignments, by using the tools available in the lab (Kinect, rendering 3D systems, stereo webcam). The possibility to use in the lab systems for acquiring, elaborating and rendering multimedia content, will allow the students to efficiently project and manage a multimedia system. The course will include dedicated seminars on practical applications of multimedia signals such as e-learning, cinema, IP-tv and mobile communications.
|
6
|
ING-INF/03
|
42
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20810258 -
New Generation Mobile Networks
|
Also available in another semester or year
|
|
|
20810288 -
12 CFU FREE OF THE STUDENT
|
12
|
|
108
|
-
|
-
|
-
|
Elective activities
|
ITA |
Algoritmi, Big Data e Machine Learning
FIRST YEAR
First semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20810256 -
Automata, Languages and Computing
(objectives)
Introduce the students to the theory of languages and, at the same time, to the theory of automata. introduce computability and complexity paradigms. At the end of the course students should know new formal methodologies, should be able to critically review, from the perspective of their expressive potential, already known methodologies and should be able to classify problems from the point of view of the resources required for their solution.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810259 -
Internet and Data Centers
(objectives)
The purpose is to provide advanced knowledge on computer networks and data centers, with methodological and technical contents. Special attention is devoted to scalability issues. At the end of the course the student is supposed to get the following concepts: inter-domain and intra-domain routing, congestion control, architectures for scalable systems. The student is also supposed to get advanced technicalities on widely adopted protocols. Finally, the student is supposed to understand the main economic and technical drivers of the internet and data centers evolution.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Optional Group:
curriculum Sistemi Informatici Complessi - curriculum ingegneria dei Dati- curriculum Algoritmi, Big Data e Machine Learning-I ANNO DUE A SCELTA - (show)
|
12
|
|
|
|
|
|
|
|
|
20810252 -
MODELS AND ALGORITHMS FOR OPTIMIZATION
(objectives)
The course aims at providing basic methodological and operative knowledge to represent and cope with decision processes and quantitative models.
|
6
|
MAT/09
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
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 |
|
20810258 -
New Generation Mobile Networks
(objectives)
To acquire general concept on new generation mobile networks (3G, 4G, 5G, 6G) as part of a communication system. To provide an overview on main operating principles of a structured mobile network, such as the available services also from a financial and economic viewpoint, quality requirements, mobility management, security, secrecy and authentication problems, localization services, power control of connected devices, access technologies from wireless devices, evolution of architecture of SW network virtualization, algorithms of array processing to allow dedicated efficient links in modern standards (5G and beyond) between terminals or smart objects connected to the IoT world.
|
6
|
ING-INF/03
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20810257 -
Diritto dei Dati
|
Also available in another semester or year
|
|
20801648 -
PROBABILITY AND STATISTICS
|
Also available in another semester or year
|
|
|
Optional Group:
curriculum Algoritmi, Big Data e Machine Learning- I anno "uno a scelta tra" - (show)
|
9
|
|
|
|
|
|
|
|
|
20810007 -
Software Systems Architecture
|
Also available in another semester or year
|
|
20810157 -
PARALLEL AND DISTRIBUTED COMPUTING
|
Also available in another semester or year
|
|
20801730 -
ARTIFICIAL INTELLIGENCE
(objectives)
The goal is to present the fundamental models, methods and techniques of various areas of Artificial Intelligence, with particular reference to heuristic search, knowledge representation and automatic reasoning, machine learning, natural language processing, computer vision. The lessons and practical exercises carried out during the course will allow the student to acquire analytical and problem solving skills on various domains of interest for the discipline.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Second semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20810266 -
Machine Learning
(objectives)
The course will allow students to deepen the methods and algorithms typical of Machine Learning (supervised, unsupervised and with reinforcement) and to use them as tools for the development of innovative technologies. In particular, aspects of the main areas of the discipline will be studied, including regression, classification and clustering. The methods and techniques of deep learning and specialized development environments will then be introduced. The course includes the development of an individual or group project that will allow students to apply the theoretical foundations learned in class to concrete problems on various domains of interest. They will be related, for example, to how to analyze large and complex datasets in various fields (e.g., Health Care, Data Science, Data Mining, Financial Analysis, Videogames, Computer Vision, etc.), create systems that adapt and improve over time (e.g., Recommender Systems), and so on. Finally, the course includes monographic seminars dedicated to various case studies.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810260 -
Tecnologie e Architetture per la Gestione dei Dati
(objectives)
The goal of the course is to present models, methods and systems that play a fundamental role in database technology, together with discussions on the recent evolution of the technology itself. The directions of development to be considered include integration of heterogeneous and autonomous systems; databases for business intelligence and decision support. After taking the course, the student will know the major features of relational database technology, the methods for data integration, and for the design of data warehouses.
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Optional Group:
curriculum Sistemi Informatici Complessi - curriculum ingegneria dei Dati- curriculum Algoritmi, Big Data e Machine Learning-I ANNO DUE A SCELTA - (show)
|
12
|
|
|
|
|
|
|
|
|
20810252 -
MODELS AND ALGORITHMS FOR OPTIMIZATION
|
Also available in another semester or year
|
|
20810208 -
Decision Support Systems and Analytics
|
Also available in another semester or year
|
|
20810258 -
New Generation Mobile Networks
|
Also available in another semester or year
|
|
20810257 -
Diritto dei Dati
(objectives)
Provide an introduction to the main principles and rules concerning data governance under Italian and European law. Study the legal distinction between personal and non personal data, with reference to the main instruments related to property, access, and circulation of data (intellectual property, trade secret, personal data protection). Analyse the issues deriving from the use of data in algorithmic decisions, both in administrative and private law contexts.
|
6
|
IUS/02
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
20801648 -
PROBABILITY AND STATISTICS
(objectives)
To provide the fundamental elements of probability theory and mathematical statistics, along with some tools of parametric statistics, which may be useful in practice.
|
6
|
MAT/06
|
54
|
-
|
-
|
-
|
Related or supplementary learning activities
|
ITA |
|
|
Optional Group:
curriculum Algoritmi, Big Data e Machine Learning- I anno "uno a scelta tra" - (show)
|
9
|
|
|
|
|
|
|
|
|
20810007 -
Software Systems Architecture
(objectives)
This unit presents software systems architecture, and it involves both methodological and technological issues. Software architecture has a fundamental role in achieving the quality (i.e., non functional) properties of software systems. In particular, the unit will study the architecture of distributed software systems, including the component-based architecture, the service-oriented architecture, and architectures for the Cloud
|
9
|
ING-INF/05
|
81
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810157 -
PARALLEL AND DISTRIBUTED COMPUTING
(objectives)
The course aims to develop the skill needed to produce computer programs for parallel and distributed computation. The theory is carefully linked to practice by implementing programming projects in a cutting edge environment
|
9
|
ING-INF/05
|
72
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801730 -
ARTIFICIAL INTELLIGENCE
|
Also available in another semester or year
|
|
SECOND YEAR
First semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
Optional Group:
Curriculum Algoritmi, Big Data e Machine Learning II anno- due a scelta di cui - (show)
|
12
|
|
|
|
|
|
|
|
|
20810006 -
ADVANCED TOPICS IN COMPUTER SCIENCE
|
Also available in another semester or year
|
|
20810261 -
Computer Graphics
(objectives)
This course aims at illustrating the modern software and hardware computer graphics architectures, and at providing mathematical, technical and methodological solutions for the development of projects concerning the visualization of data in 2D or 3D. The course will expose base concepts in computer graphics such as spaces, curves, surfaces and volumes, focusing on notions and algorithms currently used in scientific visualization, videogames, and computer animation. Moreover, this course aims at exposing details of hardware and software platforms currently in use.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810140 -
CYBERSECURITY
(objectives)
The Cybersecurity course intends to provide the student with competencies needed for understanding and tackling cybersecurity problems for ICT systems and complex organizations, to design networks and computing systems with a certain level of security, and to planning e manage activities related to cybersecurity. The course provides competences about attacks, countermeasures, cryptographic tools, applications, and methodologies in the cybersecurity field. Advanced topics in data integrity are also addressed.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810262 -
Deep Learning
(objectives)
Provide advanced and specific skills in Deep neural networks. The course consists of a theoretical part on the fundamental concepts, and laboratory activities in which these concepts are applied and developed through a software framework. At the end of the course the student will be able to: adequately train and optimize Deep neural networks; distinguish between different solutions and be able to choose and customize the most effective architectures in real-world scenarios, supervised, unsupervised or following a reinforcement learning approach.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810205 -
Digital entrepreneurship
|
Also available in another semester or year
|
|
20810223 -
DATA ENGINEERING
(objectives)
Providing skills on systems, methods, and technologies for extraction, cleaning, analyzing and integrating and management of unstructured data.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20801798 -
INTELLIGENT SYSTEMS FOR THE INTERNET
(objectives)
The course will allow students to learn various methods for the design, implementation, and testing of adaptive systems on the Web, created through Artificial Intelligence techniques, with particular reference to Machine Learning techniques. Specific attention will be paid to Information Retrieval systems, such as search engines, crawlers and document feeds. Classic retrieval models will be studied, such as the Vector Space Model and probabilistic models, document ranking techniques, as well as the PageRank algorithm used by Google. Machine Learning methods in Information Retrieval will be addressed, including techniques for Sentiment Analysis, User Modeling methods necessary for personalized search, and social search applications involving communities of individuals in activities such as content tagging and question answering. The techniques for analyzing social networks (e.g., Facebook and Twitter) will be explored, which will allow us to explore phenomena such as the spread of fake news, the filter bubble, and the polarization of users. Finally, Recommender Systems will be studied, from basic algorithms (e.g., collaborative filtering) to application scenarios (e.g., movies, books, music artists and songs).
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20802126 -
INFORMATION VISUALIZATION
|
Also available in another semester or year
|
|
Second semester
|
Course
|
Credits
|
Scientific Disciplinary Sector Code
|
Contact Hours
|
Exercise Hours
|
Laboratory Hours
|
Personal Study Hours
|
Type of Activity
|
Language
|
|
20801785 -
SKILLS FOR THE WORLD OF WORK
(objectives)
The course aims to present the main soft skills for employment access through seminars held by speakers from the production reality. The seminaris illustrate the job and career dynamics in different types of companies (startups, SMEs, multinationals) in different sectors (software integrators, service companies, product companies, insurance and banking groups, utilities). Soft skills include how to write an effective CV, how to address the job interview. The course also introduces basic notions of labor laws.
|
1
|
|
24
|
-
|
-
|
-
|
Other activities
|
ITA |
|
20802019 -
FINAL EXAM
(objectives)
https://ingegneria.uniroma3.it/didattica/collegio-informatica/lauree-e-tirocini/laurea-magistrale/
|
26
|
|
650
|
-
|
-
|
-
|
Final examination and foreign language test
|
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 |
|
20802125 -
BIG DATA
(objectives)
The goal of the course is to illustrate the modern solutions to the management of big data, very large repositories of de-structured data. Starting from the requirements of modern database applications, the course will illustrate the hardware and software architectures that have been recently proposed for the management and analysis of big data. The topics addressed in the course will include: cluster architectures, map-reduce paradigm, cloud computing, NoSQL systems, tools and languages for data analysis. Both theoretical and practical aspects will be addressed and the discussed technologies will be experimented during practical classes and through the assignment of projects.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
Optional Group:
Curriculum Algoritmi, Big Data e Machine Learning II anno- due a scelta di cui - (show)
|
12
|
|
|
|
|
|
|
|
|
20810006 -
ADVANCED TOPICS IN COMPUTER SCIENCE
(objectives)
The goal of the course is to present models, methods and systems related to the latest advances in the field of information technology able to meet the requirements of modern applications. The course is taught in English by foreign professors of high qualification
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
20810261 -
Computer Graphics
|
Also available in another semester or year
|
|
20810140 -
CYBERSECURITY
|
Also available in another semester or year
|
|
20810262 -
Deep 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 |
|
20810223 -
DATA ENGINEERING
|
Also available in another semester or year
|
|
20801798 -
INTELLIGENT SYSTEMS FOR THE INTERNET
|
Also available in another semester or year
|
|
20802126 -
INFORMATION VISUALIZATION
(objectives)
The goal of this course is that of introducing the participants to the problems and the solutions in the area of the visual exploration of abstract data, with a particular emphasis on the visual perception phenomena, on the graphic metaphors that can be exploited and on thealgorithmic methods and models that can be adopted. The
knowledge of the participants about algorithm engineering and network optimization problems will be deepened. Such a knowledge will be applied to different strains of visualization problems with a strong practical approach.
|
6
|
ING-INF/05
|
54
|
-
|
-
|
-
|
Core compulsory activities
|
ITA |
|
|
20810288 -
12 CFU FREE OF THE STUDENT
|
12
|
|
108
|
-
|
-
|
-
|
Elective activities
|
ITA |
Università Roma Tre