1047205 | CLOUD COMPUTING | 1º | 2º | 6 | INF/01 | ENG |
Obiettivi formativi General Objectives:
The purpose of the course is to give students the basic concepts of distributed systems and then to focus on cloud computing technologies. The course cover theoretical and practical aspects with a focus on real examples. At the end of the course students are supposed to be capable to chose, setup and use cloud services and to design and deploy scalable architectures and elastic applications.
Specific Objectives:
Knowledge and understanding:
On completion of the course, the student will be be able to describe and to explain
- the general concepts related to distributed systems
- the concepts of system and application virtualization
- the mechanisms and algorithms used in cloud computing
- the technologies for cloud storage
- the big data processing frameworks
- the cyber security issues and solutions in cloud computing
Applying knowledge and understanding:
On completion of the course, the student will be able:
- to design and to implement a scalable architecture and to deploy an elastic application
- to write and to present practical results in the form of technical report
- to analyze and to present scientific work
- to select, to configure and to run cloud services by using management GUI and API offered by IaaS providers
- to design and to configure elastic infrastructure and to deploy elastic applications.
- to make design choices that account for cyber security issues
Making judgements:
On completion of the course, the student will:
- be capable to assess and to compare cloud technologies and cloud services, as well as big data processing frameworks
- be capable to identify, to assess and to compare state of the art solutions
- strengthen his/her critical thinking ability
Communication skills:
On completion of the course, the student will:
- be capable to discuss on and to convey his/her own opinion on cloud technologies
- be capable to present the analysis of a selected topic to a wide audience
Learning skills:
During the course, the student will develop and will enhance his/her critical thinking skill by means of studying and analyzing scientific work and technical documentation. Moreover, the student will improve his/her capability to integrate information from different sources, e.g. books, technical/scientific papers, practical experiences.
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1047197 | DATA MANAGEMENT FOR DATA SCIENCE | 1º | 2º | 6 | ING-INF/05 | ENG |
Obiettivi formativi The main goal of the course is to present the basic concepts of data management systems. The first part of the course introduces the main aspects of relational database systems, including basic functionalities, file and index organizations, and query processing. The second part of the course aims at presenting the main non-relational approaches to data management, in particular, multidimensional data management, large-scale data management, and open data management.
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10621173 | NATURAL LANGUAGE PROCESSING AND TEXT MINING | 1º | 2º | 6 | ING-INF/05 | ENG |
Obiettivi formativi General Objectives
1. Knowledge of the main application scenarios in analyzing collections of textual data using NLP techniques.
2. Knowledge and understanding of the main methodological and analytical challenges.
3. Knowledge of the main data analysis and machine learning techniques for natural language and the primary tools available to implement them.
4. Understanding of the theoretical foundations underlying advanced techniques for textual data analysis and natural language learning.
5. Ability to translate acquired notions into programs that solve specific problems.
6. Knowledge of the main evaluation techniques and their application to practical scenarios.
Specific Objectives
Abilities
- Identify the most suitable text-mining and/or NLP techniques to address a given problem.
- Implement the proposed solution by selecting the most appropriate tools.
- Design and conduct experiments to evaluate proposed solutions under realistic conditions.
Knowledge and Understanding
- Knowledge of the main application scenarios.
- Knowledge of the main analysis techniques.
- Understanding of the theoretical and methodological assumptions underlying the main techniques.
- Knowledge and understanding of the main evaluation techniques and corresponding performance indices.
Applying Knowledge and Understanding
- Translate application requirements into concrete data-analysis problems.
- Identify the most suitable techniques and tools to address those problems.
- Qualitatively estimate the scalability of the proposed solutions in advance.
Critical and Judgment Skills
- Evaluate experimentally the effectiveness, efficiency, and scalability of proposed solutions.
Communication Skills
- Effectively describe the requirements of a problem and communicate the chosen solutions and their rationale to others.
Learning Ability
- Develop independent-study skills on course-related topics and critically consult advanced manuals and scientific literature to tackle new scenarios or apply alternative techniques.
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1047214 | DATA PRIVACY AND SECURITY | 2º | 1º | 6 | INF/01 | ENG |
Obiettivi formativi General Objectives
Ensuring the privacy of personal data, and securing the computing infrastructures, are key concerns when collecting and analyzing sensitive data sets. Example of these data sets include medical data, personal communication, personal and company-wide financial information. The course is meant to cover an overview of modern techniques aimed at protecting data privacy and security in such applications.
Specific Objectives
The students will learn the basic cryptographic techniques and their application to obtaining privacy of data in several applications, including cloud computing, statistical databases, distributed computation, and cryptocurrencies.
Knowledge and Understanding
-) Modern cryptographic techniques and their limitations.
-) Techniques for achieving privacy in statistical databases.
-) Techniques for designing cryptographic currencies and distributed ledgers.
-) Techniques for secure distributed multiparty computation.
Applying knowledge and understanding:
-) How to select the right cryptographic scheme for a particular application.
-) How to design a differentially private mechanism.
-) How to program a secure cryptosystem, or a secure smart contract, or a secure cryptographic protocol.
Autonomy of Judgment
The students will be able to judge the security of the main cryptographic applications.
Communication Skills
How to describe the security of cryptographic standards, privacy-preserving statistical databases, and blockchains.
Next Study Abilities
The students interested in research will learn what are the main open challenges in the area, and will obtain the necessary background for a deeper study of the subjects.
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10610252 | SIGNAL PROCESSING FOR MACHINE LEARNING | 2º | 1º | 6 | ING-INF/03 | ENG |
Obiettivi formativi Objectives
The goal of the course is to teach basic methodologies of signal processing and to show their application to machine learning and data science. The methods include: (i) Standard tools forprocessing time series and images, such as frequency analysis, filtering, and sampling; (ii) Sparse andlow-rank data models with applications to high dimensional data processing (e.g., sparse recovery matrix factorization, tensor completion); (iii) Graph signal processing tools, suitable to analyze and process data defined over non-metric space domains (e.g., graphs, hypergraphs, topologies, etc.) with the aim of performing graph machine learning tasks such as graph filtering, spectral clustering, topology inference from data, and graph neural networks. Finally, it is shown how to formulate and solve machine learning problems in distributed fashion, suitable for big data applications, where learning and data processing must be necessarily performed over multiple machines. Homeworks and exercises on real-world data will be carried out using Python and/or Matlab.
Specific Objectives:
1. Knowledge and understanding: Learn the basics of signal processing for machine learning and
be able to apply these concepts to real data science problems.
2. Application: Apply signal processing and machine learning techniques to real-world data sets,
using programming languages such as Python and Matlab.
3. Autonomy of judgement: Analyze the benefits and limitations of different signal processing
tools and models and determine the best methodology to use for a given data set.
4. Communication: Communicate effectively about signal processing for machine learning,
including design constraints, solutions, and potential applications.
5. Learning skills: Develop studies in the field of signal processing for machine learning,
including the ability to undertake research in this area.
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1044406 | BIG DATA COMPUTING | 2º | 1º | 6 | ING-INF/05 | ENG |
Obiettivi formativi General Objectives:
Knowledge of main application scenarios in Big Data Computing.
Knowledge and understanding of main algorithms and approaches in Big Data Computing. Knowledge of
main tools to implement them.
Understanding of theoretical foundations underlying main techniques of analysis
Ability to implement the aforementioned algorithms, approaches and techniques and to
apply them to specific problems and scenarios.
Knowledge of main evaluation techniques and their application to practical scenarios.
Specific objectives:
Ability to:
- identify the most suitable techniques to address a data analysis problem where
data dimensionality is concern;
- implement the proposed solution, identifying the most appropriate design and
implementation tools, among available ones;
- Design and implement experiments to evaluate proposed solutions in realistic settings;
Knowledge and understanding:
- knowledge of main application scenarios;
- knowledge of main techniques of analysis;
- understanding of methodological and theoretical foundations of main analysis techniques;
- knowledge and understanding of main evalutation techniques and corresponding
performance indices
Apply knowledge and understanding:
- being able to translate application needs into specific data analysis
problems;
- being able to identify aspects of the problem for which data dimensionality
might play a critical role;
- being able to identify the most suitable techniques and tools to addresse the
aforementioned problems;
- being able to estimate in advance, at least qualitatively, the degree of scalability
of proposed solutions;
Critical and judgment skills:
Being able to evaluate, also experimentally, the effectiveness and efficiency of
proposed solutions
Communication skills:
Being able to effectively describe the requirements of a problem and
provide to third parties the relative specifications, design choices and
the reasons underlying these choices.
Learning ability:
The course will facilitate the development of skills for the independent
study of topics related to the course. It will also allow students to identify
and critically examine material contained in andvanced manuals and/or scientific
literature, allowing them to face new application scenarios and/or apply alternative
techniques to known ones.
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10621189 | ADVANCED MACHINE LEARNING AND COMPUTER VISION | 2º | 1º | 6 | INF/01 | ENG |
Obiettivi formativi The course will present to students the advanced and most recent concepts of Machine Learning and their application in Computer Vision via deep neural network (DNN) models. It will include theory and practical coding, as well as a final hands-on project. Towards the coding assignments and the final project, the students will work in teams and present their ideas and project outcome to the class. At the end of the course, students will be familiar with state-of-the-art DNN models for multiple tasks and multi-task objectives, as well as generalization and the effective use of labeled and unlabelled data for learning, self-supervision, and meta-learning.
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10621190 | NETWORK SCIENCE AND COMPLEX SYSTEMS | 2º | 1º | 6 | INF/01 | ENG |
Obiettivi formativi General goals
This course provides an advanced introduction to complex systems science as a framework for analyzing emergent dynamics in socio-technical systems. Grounded in real-world case studies drawn from cutting-edge scientific research (e.g., misinformation diffusion, mobility, algorithmic exposure, and systemic resilience), the course combines theoretical models with data-driven analysis to understand and simulate collective behavior.
Specific goals
Introduce complex systems as a conceptual and analytical framework for studying dynamic, interacting systems.
Provide foundational and advanced tools in network science to represent structure, interactions, and systemic transformations.
Develop the ability to build, simulate, and validate computational models of real-world phenomena.
Explore contemporary case studies involving large-scale social and technological systems.
Knowledge and understanding
Students will acquire a solid understanding of:
Core models of complex systems (self-organization, criticality, nonlinear dynamics).
Structural properties of complex networks, including multilayer and time-evolving networks.
Dynamical processes on networks: diffusion, opinion dynamics, polarization, resilience.
Percolation theory as a tool to understand fragility and cascading processes.
Methodological and ethical issues in modeling real-world socio-technical systems.
Applying knowledge and understanding
Students will be able to:
Model and simulate emergent phenomena over complex network structures.
Build networks from empirical data (e.g., social media, mobility, interaction systems).
Apply and test theoretical models against real datasets.
Develop data-driven modeling pipelines for analyzing collective dynamics.
Design and deliver a final project based on real-world case studies.
Critical and judgmental abilities
Students will develop the capacity to:
Evaluate the strengths and limitations of modeling approaches for complex systems.
Analyze the interplay between structure and dynamics in observed phenomena.
Identify causal patterns and avoid spurious correlations.
Critically reflect on the implications of modeling choices in real-world applications.
Communication skills
Students will be able to:
Present models, simulations, and results effectively in both written and oral form.
Use technical terminology from complex systems and network science appropriately.
Argue and interpret findings clearly within interdisciplinary and data-driven contexts.
Learning ability
Students will develop:
The ability to autonomously engage with new developments in complex systems research.
Skills to integrate novel computational and theoretical tools.
A systemic and empirical mindset to approach evolving real-world challenges.
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10616533 | GRAPH MINING AND APPLICATIONS | 2º | 2º | 6 | ING-INF/05 | ENG |
Obiettivi formativi Graphs have applications in multiple areas, including social networks, bioinformatics, network medicine, computational chemistry, and they can be used to provide tools in these areas.
The course will present models and algorithms for the analysis of graphs as with applications on various areas. The goal at the end of the course, is for student to know algorithms and frameworks that can allow them to analyze large graph data.
- Knowledge of basic algorithms
- Programming
- Linear algebra
- Probability
- Neural networks
• Theoretical algorithms for graph modeling and analysis:
◦ Real graph properties and models (Gnp, preferential attachment, Kleinberg’s reachability)
◦ Models for propagation (linear threshold, cascade) and for opinion formation
◦ Homophily and influence and algorithms for identifying and distinguishing
◦ Influence maximization
◦ Algorithms for graph alignment
◦ Dense subgraphs, community detection, graph minors
◦ Graph summarization and sampling
• Machine-learning approaches:
◦ Label propagation
◦ Graph transformers
◦ Knowledge-graph emdeddings
◦ Models for analysis of temporal graphs
◦ Explainability
• Architectures for handling large graph data:
◦ Spark GraphsX
◦ AWS Neptune
◦ AWS GraphStorm
◦ Neo4J
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10621435 | SMART ENVIRONMENTS AND CYBER PHYSICAL SPACES | 2º | 2º | 6 | ING-INF/03 | ENG |
Obiettivi formativi The aim of this course is to provide an overview of the vast world of wireless and wired technologies that will be used in smart environments and cyber-physical spaces. These technologies will enable the development of network infrastructures and platforms for processing digital, multimedia, and extended reality information, applied in urban and intelligent environments.
Recent advancements in fields such as edge computing, machine learning, wireless networks, and sensor networks allow for various smart environmental applications in everyday life. The primary objective of this course is to present and discuss the latest developments in the Internet of Things area, particularly focusing on technologies, architectures, algorithms, and protocols for smart environments, with an emphasis on real-world applications. The course will cover communication and networking aspects, as well as multimedia and extended reality data processing for application design. Two case studies in the domain of smart environments will be presented: vehicular traffic monitoring for ITS applications, and low-power wireless networks. For both cases, tools, models, and methodologies for designing smart environment applications will be provided.
SPECIFIC OBJECTIVES
• Knowledge and understanding: Understand recent developments in the Internet of Things, particularly technologies, architectures, algorithms, and protocols for smart environments, with a focus on applications and processing platforms. Understand recent advancements in the representation of multimedia and extended reality data.
• Applying Knowledge and Understanding
Students will learn to apply the knowledge gained in real-world IoT platform design. This includes everything from data acquisition and networking solutions to the design of practical smart environment applications, such as vehicular traffic monitoring, networked systems (smart grids and smart monitoring) and processing for extended reality integration.
• Making Judgements
Students will be able to analyze the benefits and challenges associated with smart environment technologies and applications, considering factors such as communication constraints, big data processing, and the integration of extended reality in IoT systems. They will also evaluate the trade-offs of different solutions based on practical case studies.
• Communication Skills
Students will be able to present their projects effectively, discussing design constraints, solutions, and the potential applications of smart environment technologies. They will also gain experience in explaining complex topics such as IoT networking, signal sampling, localization, and XR technologies.
• Learning Skills
The course will prepare students for more advanced studies and research in the fields of ambient intelligence and smart spaces and next-generation IoT systems. They will gain the skills necessary for continued development in the ever-evolving landscape of smart environments and IoT networking and processing.
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