Tutto il materiale è disponibile nel seguente insegnamento
- Docente: GOTTARD ANNA
Tutto il materiale è disponibile nel seguente insegnamento
B031115 (B236) - STATISTICAL ANALYSIS OF NETWORK DATA 2023-2024
- Docente: MARINO MARIA FRANCESCA
Tutto il materiale è disponibile nel seguente insegnamento
B024206 (B236) - STATISTICS FOR SPATIAL DATA (CURRICULUM: GENERALE - E96) 2023-2024
- Docente: BOCCI CHIARA
- Docente: DREASSI EMANUELA
This course provides an up-to-date survey of developments and practice in computer security. It covers the central problems that confront security designers and security administrators, that is, defining the threats to computer and network systems, evaluating the relative risks of these threats, and developing cost-effective and user-friendly countermeasures. The main arguments are: Computer Security Technology and Principles; Software Security and Trusted Systems; Management Issues.
- Docente: PUGLIESE ROSARIO
- Docente: PECORELLA TOMMASO
Tutto il materiale è disponibile nel seguente insegnamento
B029807 (B236) - ALGORITMI E PROGRAMMAZIONE PER L'ANALISI DEI DATI 2023-2024
- Docente: MARINO ANDREA
Tutto il materiale è disponibile nel seguente insegnamento
B018782 (B077) - PROCESSI STOCASTICI (CURRICULUM: GENERALE - C75) 2023-2024
- Docente: AVENA LUCA
- Docente: VESPRI VINCENZO
Tutto il materiale è disponibile nel seguente insegnamento
- Docente: CASSESE ALBERTO
- Docente: VISCARDI CECILIA
Tutto il materiale è disponibile nel seguente insegnamento
B016441 (B214) - STATISTICAL INFERENCE (CURRICULUM: DEVELOPMENT ECONOMICS - E20) 2023-2024
- Docente: CASSESE ALBERTO
- Docente: CASTELLANA DANIELE
- Docente: BETTINI LORENZO
- Docente: CECCARELLI ANDREA
- Docente: LOLLINI PAOLO
The course aims to provide general knowledge about distributed programming, methodologies, and tools. The course starts by reviewing the basic concepts relevant to the course, then presents how distributed systems are organized and basic communication mechanisms. The course then introduces the basic techniques for developing modern distributed systems using the web, IoT, and mobile technologies. The course has a hands-on approach.
- Docente: GALLETTA LETTERIO
In questo insegnamento è presente il materiale relativo anche ai seguenti mutuati:
B024308 (B070) - SOFTWARE ARCHITECTURES AND METHODOLOGIES - CANALE C (CURRICULUM: BIG DATA AND DISTRIBUTED SYSTEMS - F026) 2023-2024
B024308 (B070) - SOFTWARE ARCHITECTURES AND METHODOLOGIES - CANALE C (CURRICULUM: ADVANCED COMPUTING - F027) 2023-2024
B024308 (B070) - SOFTWARE ARCHITECTURES AND METHODOLOGIES - CANALE C (CURRICULUM: COMPUTING SYSTEMS AND NETWORKS - F025) 2023-2024
- Docente: VICARIO ENRICO
In questo insegnamento è presente il materiale relativo anche ai seguenti mutuati:
B031237 (B070) - RESILIENCY, REAL TIME AND CERTIFICATION (CURRICULUM: ADVANCED COMPUTING - F027) 2023-2024
- Docente: BONDAVALLI ANDREA
- Docente: CECCARELLI ANDREA
- Docente: ZOPPI TOMMASO
- Docente: COSTA GABRIELE
Today, supporting the digital transformation of our world, software plays a pivotal role in businesses, industries, and everyday life. Therefore, as software systems become more complex and interconnected, ensuring their reliability, performance, and security is needed and here is where software analysis takes part.
However, as software projects grow in size and complexity, manual analysis methods become impractical and time-consuming. Traditional approaches to software analysis often struggle to keep pace with the rapid evolution of software systems and the sheer volume of code produced. This is where machine learning emerges as a powerful tool in the software engineer's toolkit.
The course will provide an introduction to machine learning methods and their application to software analysis tasks.
The students will know how to decide the appropriate machine learning method should be used on specific program analysis tasks
- Docente: PINELLI FABIO
In questo insegnamento è presente il materiale relativo anche ai seguenti mutuati:
B024322 (B070) - SOFTWARE DEPENDABILITY (CURRICULUM: COMPUTING SYSTEMS AND NETWORKS - F025) 2023-2024
B024322 (B070) - SOFTWARE DEPENDABILITY (CURRICULUM: ADVANCED COMPUTING - F027) 2023-2024
- Docente: FANTECHI ALESSANDRO
In questo insegnamento è presente il materiale relativo anche ai seguenti mutuati:
B031292 (B241) - STOCHASTIC MODELS 2023-2024
B031361 (B070) - QUANTITATIVE EVALUATION OF STOCHASTIC MODELS (CURRICULUM: BIG DATA AND DISTRIBUTED SYSTEMS - F026) 2023-2024
B031361 (B070) - QUANTITATIVE EVALUATION OF STOCHASTIC MODELS (CURRICULUM: MULTIMEDIA SYSTEMS - F024) 2023-2024
B031396 (B070) - MODULO: QUANTITATIVE EVALUATION OF STOCHASTIC MODELS (CURRICULUM: COMPUTING SYSTEMS AND NETWORKS - F025) 2023-2024
B031396 (B070) - MODULO: QUANTITATIVE EVALUATION OF STOCHASTIC MODELS (CURRICULUM: ADVANCED COMPUTING - F027) 2023-2024
- Docente: PICANO BENEDETTA
- Docente: VICARIO ENRICO
- Docente: TIEZZI FRANCESCO
- Docente: COSTA GABRIELE
- Docente: TIEZZI FRANCESCO
- Docente: MARINO MARIA FRANCESCA
- Docente: MENCHETTI FIAMMETTA
Please, refer to this e-learning Moodle page also for the course:
B030633 (B226) - MODULO: NUMERICAL METHODS FOR SCIENTIFIC COMPUTING 2023-2024
- Docente: GURIOLI GIANMARCO
- Docente: PATRIZI FRANCESCO
The course aims at providing the students with the basic notions on mathematical modeling and focuses on linear programming, network flows optimization and mixed-integer linear programming. At the end of the course, students will be able to classify different mathematical programming problems, knowing the main results related to the characterization of their solutions. The students will also be able to formulate mixed-integer and network optimization problems and to use standard algorithms to deal with specific application contexts.
- Docente: CAPPANERA PAOLA
- Docente: DE SANTIS MARIANNA
This course aims at introducing classical problems in systems-and-control theory, such as the analysis of dynamical systems and the associated controller synthesis, estimation and state reconstruction problems. Once discussed how to tackle the underlying problems with standard mathematical tools, they will hence be addressed and solved using data-driven and machine learning techniques, emphasizing their pros and cons. Frontal lectures will be alternated with hands-on sessions involving programming in MATLAB, Python, or Julia.
- Docente: FABIANI FILIPPO