Data Science

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General goals: The aim of the course is to introduce students to the theory and applications of optimization techniques for machine learning problems. Also, students are expected to acquire knowledge about standard models used in machine learning, such as Deep Neural Networks and Support Vector Machines.

Specific goals: The course will put a special emphasis on convex optimization techniques which play a key role in data sciences. The objective of this course is to provide basic tools and methods at the core of modern nonlinear convex optimization. Starting from the gradient descent method we will cover some state of the art algorithms, including proximal gradient methods, accelerated methods, stochastic subgradient method and randomized block-coordinate descent methods, which are nowadays very popular techniques to solve machine learning and inverse problems. The course will also cover topics in statistics in high dimension which is the typical scenario in machine learning problems.

Knowledge and understanding: the student will learn about (1) mathematical formulation of the machine learning problem, (2) the latest optimization algorithms for solving machine learning problems and extract information from data (3) understanding the theory of convergence of such algorithms (4) develop computational skills for handling several important problems in data science.

Applying knowledge and understanding: through several examples from applied sciences and lab sessions, the student will appreciate the importance of optimization techniques and will understand which algorithm is most appropriate to use in each context.

Critical and judgmental skills: the student will be able to tackle with rigor a number of significant optimization problems and algorithms so as to become fully aware of the technicalities and main ideas behind the various approaches. This will stimulate the student's independent judgment.

Communication skills: by studying the theoretical and practical aspects of optimization techniques the student will learn gradually to communicate with rigor and clarity. She will also learn that a proper understanding of the mathematical aspects of data science is one of the main skills to achieve effective communication.

Learning skills: students will have the chance to have additional details on some specific topics.

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GENERAL DESCRIPTION
The goal of this course is to provide an overview of stochastic processes, with applications to Data Science in
mind. Stochastic processes and probability are important in data science because they can be used to model
and analyze a wide range of data sets, from financial data to sensor data. The course will cover three parts: a
gentle introduction to combinatorial stochastic processes, Gaussian processes, and probabilistic causality.
Programming in R, Matlab or Python is useful, but it is not essential. Programs in R will be used.
SPECIFIC OBJECTIVES:
1. Knowledge and understanding: Understand the basics of combinatorial stochastic processes and
Gaussian processes, and their applications in data science. Understand the fundamentals of
probabilistic causality and be able to apply these concepts to real-world data science problems.
2. Application: Apply stochastic process to real-world data sets, using programming languages such as R,
Matlab, or Python.
3. Autonomy of judgement: Analyze the benefits and limitations of different stochastic process models
and determine the best model to use for a given data set.
4. Communication: Communicate effectively about stochastic processes, including design constraints,
solutions, and potential applications.
5. Learning skills: Develop studies in the field of stochastic processes for data science, including the ability
to undertake research in this area.

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Devising new machine learning methods and statistical models is a fun and extremely fruitful “art”. But these powerful tools are not useful unless we understand when they work, and when they fail. The main goal of statistical learning theory is thus to study, in a
statistical framework, the properties of learning algorithms mainly in the form of so-called error bounds.
This course introduces the techniques that are used to obtain such results, combining methodology with theoretical foundations and computational aspects. It treats both the basic principles to design successful learning algorithms and the “science” of analyzing an algorithm’s statistical properties and performance guarantees.
Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own data analyses.
Methods for a wide variety of applied problems will be explored and implemented on open-source software like R (www.r-project.org), Keras (https://keras.io/) and TensorFlow (https://www.tensorflow.org/).
Knowledge and understanding

On successful completion of this course, students will:
know the main learning methodologies and paradigms with their strengths and weakness;
be able to identify a proper learning model for a given problem;
assess the empirical and theoretical performance of different learning models;
know the main platforms, programming languages and solutions to develop effective implementations.

Applying knowledge and understanding
Besides the understanding of theoretical aspects, thanks to applied homeworks and a final project possibly linked to hackathons or other data analysis competitions, the students will constantly be challenged to use and evaluate modern learning techniques and algorithms.

Making judgements
On successful completion of this course, students will develop a positive critical attitude towards the empirical and theoretical evaluation of statistical learning paradigms and techniques.

Communication skills
In preparing the report and oral presentation for the final project, students will learn how to effectively communicate original ideas, experimental results and the principles behind advanced data analytic techniques in written and oral form. They will also understand how to offer constructive critiques on the presentations of their peers.

Learning skills
In this course the students will develop the skills necessary for a successful understanding as well as development of new learning methodologies together with their effective implementation. The goal is of course to grow an active attitude towards continued learning throughout a professional career.

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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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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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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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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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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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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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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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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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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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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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General Objectives of the course

The general objectives of the course are:
- Present a general framework for the development of quantitative models for economic analysis and management;
- Provide the basic concepts and a guide to analyse the specialised literature;
- Propose a unified framework on the main methodologies available to compare the productivity and efficiency of Decision Making Units (DMUs);
- Introduce to the relevant roles played by the data for the development of effective quantitative models of socio-economic systems;
- Make an introduction to the main softwares available to implement the quantitative models presented during the course;
- Provide laboratory sessions to implement the quantitative models presented during the course in practice;
- Present several applications in the field of economics and management, including public sector services as potential group project works, to be developed by the students according to their personal interest and background;
- Interact with students through seminars, assisted laboratory, oral presentations and the realization of a project work on real data.

Specific objectives of the course

• Knowledge and understanding: demonstrate the knowledge of the basic methods for the development of quantitative models for economic analysis and management.

• Ability to apply knowledge and understanding: to be able to develop quantitative economic models on the base of the knowledge and techniques learned during the course.

• Judgment autonomy: to be able to develop a quantitative economic model with critical spirit, choosing the appropriate method and correctly implementing it.

• Communication skills: being able to communicate the results of the analysis and its information to different types of interlocutors.

• Learning skills: to develop the necessary skills to apply and develop autonomously the methods and models learned during the course.

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1) Knowledge and understanding
During the lectures of Data-driven Economics, students acquire the basic theoretical elements of
econometric analysis. Theoretical lectures are aimed at guiding students in the acquisition of the basics
of simple and multiple regression models, starting from the relative assumptions, and then proceeding
with the estimation and inference procedures. The course contents cover both the estimation of linear
and non-linear models and the analysis of both cross-sectional and longitudinal data.
2) Applying knowledge and understanding
The students of the Data-driven Economics course are able to apply the notions acquired during the
theoretical lectures to a wide range of problems of an empirical nature. They acquire the ability to build
econometric models aimed at giving empirical content to economic relations and are also able to
establish a causal link between two or more variables in the economic field.
3) Making judgements
Students are encouraged to critically discuss empirical studies published in the economic/managerial
field in the classroom. The Data-driven Economics course also includes a laboratory in which students
apply the acquired knowledge of econometrics to the estimation of empirical models carried out using
data made available by the teacher.
4) Communication skills
At the end of the course, students are able to illustrate and explain the strengths and weaknesses of a
wide range of empirical methodologies to a variety of heterogeneous interlocutors in terms of training
and professional role. The acquisition of these skills is verified and evaluated not only during the final
exam, by means of a written test and a possible oral test, but also during flipped class sessions in which,
individually or in groups, students are called to present empirical studies published in the
economic/managerial field.
5) Learning skills
Students acquire the ability to independently conduct empirical analyses by building econometric
models to be estimated using data with diversified structures. The tools provided by the course allow
for the analysis of systems in which a large number of factors simultaneously contribute to explaining
their states and impact assessments that take into account the uncertainty and risk inherent in the
application of policies. The acquisition of these skills is verified and evaluated during the final exam, by
means a written test and a possible oral test, in which the student can be called to discuss empirical
problems on the basis of the topics covered and the reference material distributed during the course.

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General goals: The course is intended to provide a general overview of EU law and policies relevant to digital data, putting the recent legislation in context and in comparison with the less recent legislation so as to highlight the gradual shift from the governance problems characteristic of the so-called information society (concerning electronic reproduction and communication of information) to the so-called data-driven economy (concerning, in addition to reproduction and communication of digital information, the phenomenon of automatic production of digital data).

Specific goals: Specific goals will include the supply of basic notions relevant to:
1. GDPR (General Data Protection Regulation)
2. Open Data Directive
3. DGA (Data Governance Act)
4. Data Act
5. DSA (Digital Services Act) & DMA (Digital Markets Act)
6. AI Act (Artificial Intelligence Act)

Knowledge and understanding: Students will acquire general knowledge and understanding about EU law and policies (over the years) concerning the market of digital data

Applying knowledge and understanding: Students will acquire applying knowledge and understanding in connection with the application and enforcement of EU data law in a number of fields

Critical and judgmental abilities: Students will acquire critical judgement and abilities, in particular as regarding methodology, categories and legal concepts so as to be able to “navigate” within EU and national data law (see under Specific goals above and Learning ability below)
Communication skills: Students will acquire sufficient communication skills relevant to legal terminology in the field of data law

Learning ability: students will be able to interpret and understand the interplay between the regulations and directives specifically studied and forming the content of the program (as listed under Specific goals, above) and the many other sources of Union and national law dealing with digital data and digital infrastructures in a number of fields and technological sectors (such as legislation on health data and financial data as well as legislation on technologies and infrastructures such as decentralized ledger technologies, cloud technologies, internet of things) as well as in specific areas of public and private law (such as administrative law - eg public procurement for AI applications, golden power legislation - criminal law, intellectual property law, corporate law, consumer law, contract law, tort law).

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For the objectives of this teaching, check the individual modules I and II.

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General:
The aim of the course is to introduce students to the economics of digital markets, which are often
dominated by large platforms. Students are expected to gain insight into the main features of
digital markets, such as: network effects; complementarity, compatibility, and standards;
switching costs and lock in; scale economies. They are also expected to comprehend and assess
how these specific features of technology and demand can affect market structure, firms’
strategies, and public policy in digital markets. At the end of the course, students should be able to
use methods and models of microeconomics and industrial organization to understand and
analyze the competitive dynamics in digital markets.

Specific:
The course is organized in two modules.
Based on the foundations laid down by the first module, the second module investigates how
specific factors such as network effects, switching costs, lock in, and scale economies can shape
firms’ strategies in digital markets (including complementarity and compatibility choices) as well as
affect the scope for public policy.

Knowledge and understanding:
The course introduces students to the new information economy and the economics of digital
markets. Students are expected to gain insight into how the specific features of technology and
demand affect market structure, firms’ strategies and business models, as well as public policy in
digital markets.

Applying knowledge and understanding:
By the end of the course, students should be able to use methods and models of microeconomics
and industrial organization to understand and analyze the competitive dynamics in the new
information economy, and specifically in digital markets.

Making judgements:
Lectures, practical exercises and problem-solving sessions will provide students with the ability to
assess the main strengths and weaknesses of theoretical models when used to explain empirical
evidence and case studies in the new information economy.

Communication:
By the end of the course, students are able to point out the main features of the new information
economy and digital markets, and to discuss relevant information, ideas, problems and solutions
both with a specialized and a non-specialized audience. These capabilities are tested and
evaluated in the final written exam and possibly in the oral exam.

Lifelong learning skills:
Students are expected to develop those learning skills necessary to undertake additional studies
on relevant topics in the field of the new information economy with a high degree of autonomy.
During the course, students are encouraged to investigate further any topics of major interest, by
consulting supplementary academic publications, specialized books, and internet sites. These
capabilities are tested and evaluated in the final written exam and possibly in the oral exam,
where students may have to discuss and solve some new problems based on the topics and
material covered in class.

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General:
The aim of the course is to introduce students to the economics of digital markets, which are often
dominated by large platforms. Students are expected to gain insight into the main features of
digital markets, such as: network effects; complementarity, compatibility, and standards;
switching costs and lock in; scale economies. They are also expected to comprehend and assess
how these specific features of technology and demand can affect market structure, firms’
strategies, and public policy in digital markets. At the end of the course, students should be able to
use methods and models of microeconomics and industrial organization to understand and
analyze the competitive dynamics in digital markets.

Specific:
The course is organized in two modules. The first module describes the main features of digital
markets. Then, it addresses the issue of market power and related implications, with a focus on
monopoly pricing and price discrimination -personalized pricing, menu pricing (non-linear pricing
and versioning), and group pricing.

Knowledge and understanding:
The course introduces students to the new information economy and the economics of digital
markets. Students are expected to gain insight into how the specific features of technology and
demand affect market structure, firms’ strategies and business models, as well as public policy in
digital markets.

Applying knowledge and understanding:
By the end of the course, students should be able to use methods and models of microeconomics
and industrial organization to understand and analyze the competitive dynamics in the new
information economy, and specifically in digital markets.

Making judgements:
Lectures, practical exercises and problem-solving sessions will provide students with the ability to
assess the main strengths and weaknesses of theoretical models when used to explain empirical
evidence and case studies in the new information economy.

Communication:
By the end of the course, students are able to point out the main features of the new information
economy and digital markets, and to discuss relevant information, ideas, problems and solutions
both with a specialized and a non-specialized audience. These capabilities are tested and
evaluated in the final written exam and possibly in the oral exam.

Lifelong learning skills:
Students are expected to develop those learning skills necessary to undertake additional studies
on relevant topics in the field of the new information economy with a high degree of autonomy.
During the course, students are encouraged to investigate further any topics of major interest, by
consulting supplementary academic publications, specialized books, and internet sites. These
capabilities are tested and evaluated in the final written exam and possibly in the oral exam,
where students may have to discuss and solve some new problems based on the topics and
material covered in class.

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General goals: understanding of the work on big data in the field of official statistics.

Specific goals: learning general concepts, specific methods and typical topics of official statistics

Knowledge and understanding: knowledge of the main methodologies used in official statistics and understanding of their applications in this field

Applying knowledge and understanding: ability to apply these methodologies to the typical topics of official statistics

Critical and judgmental skills: application skills on operational topics

Communication skills: ability to present results in a meaningful and dynamic way

Learning ability: ability to learn general concepts and know how to apply them

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General objectives. The general objectives of the course are: i) to provide students with a hands-on experience with basic biological concepts and common bioinformatics tools and databases; ii) to introduce students to the on-the-field application of networks in biology and medicine.
Specific objectives. Students are expected to acquire basic biology knowledge and skills, to understand the role of networks in the study of physiological mechanisms and diseases; to understand how to use network medicine algorithms and procedures.
Knowledge and understanding. The course will include theory and hands-on projects. Students will be trained in the basic theory and application of programs used for database searching, biological network inference and analysis.
Apply knowledge and understanding. At the end of the course students will have become familiar with basic biological concepts and bioinformatics databases and tools. Furthermore, on successful completion of this course, students will understand the use of networks as a paradigm for disease expression and course.
Critical and judgment skills. At the end of the course, students will be able to critically analyse the results of their analysis.
Communication skills. The students will be required to produce reports describing the hands-on projects with specific sections for the description of the obtained results and their discussion.
Learning ability. The projects will be developed in small groups encouraging team building. All the acquired abilities will be checked in a final oral exam during which a good division of teamwork will be rewarded.

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General objectives. Digital data sources and digital traces of human behaviour have the potential to provide local and timely information about disease and health dynamics at the population level. The general aim of the course is to introduce students to the analysis of epidemiological and omics data and to the use of computational approaches for medical/clinical purposes.
Specific objectives. The course consists of two modules. The first module will deal with the opportunities and challenges of mining digital data sources for epidemiological and public health signals and will provide an overview of the state of the art of this emerging field. The second module will focus on “precision medicine”, an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. With the second module, the students are expected to acquire basic biology knowledge and skills and to become familiar with the analysis and integration of omics data.
Knowledge and understanding. The course will include theory and hands-on lectures. Students will be trained in the basic theory for the identification of gene interactions and in the use of network science.
Apply knowledge and understanding. At the end of the course students will have become familiar with basic biological concepts, with the analysis of omics and epidemiological data and with the use of networks for the investigation of infectious disease dynamics and disease etiology, diagnosis, and treatment.
Critical and judgment skills. At the end of the course, students will be able to critically analyse the results of their analysis.
Communication skills. The students will be required to produce reports describing hands-on projects with specific sections for the description of the obtained results and their discussion.
Learning ability. The projects will be developed in small groups encouraging team building.

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General goals: the course is intended as a broad overview to neural networks, as used today in a number of applicative fields. It provides a strong theoretical and practical understanding of how neural networks and modern deep networks are designed and implemented, highlighting the most common components, ideas, and current limitations.
Specific goals: from a theoretical point of view, we will review the general paradigm of building differentiable models that can be optimized end-to-end with gradient descent from data. We will then overview essential components to design architectures able to work on images (convolutive layers), sequences (recurrent layers), and sets (transformer layers). The last part of the course will then focus on a selection of important research topics, including graph neural networks, continual learning, and generative models.
Knowledge and understanding: at the end of the course, the student will have a broad understanding of how deep networks work in practice, with the capability of implementing new components from scratch, re-using existing models, or designing new architectures for problems beyond the overview of the course.
Applying knowledge and understanding: students will acquire the ability to implement neural network models using modern deep learning frameworks. They will learn how to preprocess data, choose suitable architectures, train and evaluate models, and debug common issues in neural network training. The emphasis will be on practical problem-solving, enabling students to apply theoretical knowledge to real-world tasks in vision, language, and structured data domains.
Critical and judgment skills: the student is expected to be able to analyze a new problem requiring machine learning, and design the appropriate neural network based solution to tackle it, understanding both its strengths and its drawbacks.
Communication skills: the course will foster communication skills in terms of being able to describe (in both a technical and non-technical way) the mathematics underlying the models, as long as writing clear and understandable code for its implementation.
Learning ability: beyond the topics of the course, the student will be able to autonomously study new topics on the research frontier, and navigate the current scientific literature and software panorama.

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This course has the target of providing the students with the modern techniques of measuring quantitatively advanced topics in economic statistics. In particular our focus will be on three main interrelated directions: 1) the analysis of production and efficiency, specifically in the private but also in the public sectors, 2) economic dynamics of sectorial systems founded on micro data, 3) growth, ICT and technology in the modern economy.
This course uses statistical methods, both stochastic and deterministic, to analyze topics such as productivity, efficiency and growth at micro, sectorial, and for coherence at macro level. We first take into exam data from firms that will be useful for the mentioned three-levels study, then, as regards the efficiency analysis of productive units, such data will be employed in order to evaluate mergers and acquisitions of plants and firms and management of productive factors. Efficiency will be evaluated from the sides of costs, profits and revenues. As for the sectorial analysis, static and dynamic models will be considered to allow for forecasts and simulations in each sector for variables like production, labour, capital, raw materials, prices and capital gains. As a consequence, an aggregate analysis on the production, growth and prices will follow. We also deal with ICT and technical progress in the production process considering how and if the associated externalities are effective. We will use the following techniques for data analysis: accounting rules for the database, panel data econometrics, time series analysis for systems of equations, methods for differential equation systems. Topics on private and also public sectors will contribute to explain the relationship between economic structure and the actual crisis. Specifically, lectures also include the examinations of cases study concerning the efficiency and productivity analysis on the recent patterns of the banking sector in the international context.

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The course finds its motivation in the great availability and relevance of geospatial data (in particular big data), and it aims to provide the fundamentals on the main methodologies and techniques currently available for their acquisition, verification, analysis, storage and sharing.
In fact, the vast majority (a percentage close to 80%) of the currently available data has a geographical connotation, is intrinsically linked to a position; they are therefore named geospatial data. Furthermore, the ever-increasing availability of sensors capable of acquiring geospatial data, allowing the acquisition of larger and larger amounts of data, raises several important issues related to the correct, efficient and effective use of these geospatial big data.
Special attention is given to data coming from Global Navigation Satellite Systems (GNSS), Photogrammetry and Remote Sensing, Volunteered Geographic Information (VGI) and crowdsourcing, both regarding their analysis and management with freely available software and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine).

Knowledge and understanding
Students who have passed the exam will know the fundamentals on the main methodologies and techniques currently available for geospatial data acquisition, verification, analysis, storage and sharing, with focus on Global Navigation Satellite Systems (GNSS), Photogrammetry and Remote Sensing, and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine), being also aware of the relevant resources represented by Volunteered Geographic Information (VGI) and crowdsourcing

Applying knowledge and understanding
Students who have passed the exam will be able to plan and manage the acquisition, verification, analysis, storage and sharing of geospatial data necessary to solve interdisciplinary problems, using Global Navigation Satellite Systems (GNSS), Photogrammetry and Remote Sensing, and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine), being also aware of the relevant additional contributions which can be supplied by Volunteered Geographic Information (VGI) and crowdsourcing

Making judgment
Students will acquire autonomy of judgment thanks to the skills developed during the execution of the numerical and practical exercises that will be proposed on three main topics of the course (Global Navigation Satellite Systems, Photogrammetry and Remote Sensing, Google Earth Engine)

Learning skills
The acquisition of basic methodological skills on the topics covered, together with state-of-the-art operational skills, favors the development of autonomous learning skills by the student, allowing continuous, autonomous and thorough updating.

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The module aims at providing a general background on the remote sensing systems for Earth Observation from space‐borne platforms and on data processing techniques. It describes, using a system approach, the characteristics of the system to be specified to fulfil the final user requirements in different domains of application. Remote sensing basics and simple wave‐interaction models useful for data interpretation are reviewed together with technical principles of the main remote sensors. The course also provides an overview of the most important applications and bio‐geophysical parameters (of the atmosphere, the ocean and the land) which can be retrieved. The most important techniques for data processing and product generation, also by proposing ractical exercises using the computer, are analysed together with an overview of the main Earth Observation satellite missions and the products they provide to the final user.

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Obiettivi generali:

Il corso presenterà un'ampia panoramica di argomenti all’intersezione di informatica, scienza dei dati ed economia, sottolineando l’efficienza, la robustezza e le applicazioni ai mercati online emergenti. Introdurrà i principi della teoria algoritmica dei giochi e della progettazione dei meccanismi economici, della progettazione algoritmica del mercato, nonché dell'apprendimento automatico nei giochi e nei mercati. Dimostrerà applicazioni a casi di studio nella ricerca sul Web e nella pubblicità online, nell'economia delle reti, nei dati, nelle criptovalute e nei mercati dell'intelligenza artificiale.

Risultati specifici:
Conoscenza e comprensione:
I principi algoritmici e matematici dell’economia alla base della progettazione e del funzionamento di mercati online efficienti e robusti. L'applicazione di questi principi in esempi concreti di mercati online.

Applicare conoscenza e comprensione:
Essere in grado di progettare e analizzare algoritmi per concrete applicazioni dei mercati online rispetto ai requisiti di efficienza e robustezza.
Capacità critiche e di giudizio:
Essere in grado di valutare la qualità di un algoritmo per applicazioni nel mercato online, discriminando gli aspetti di modellizzazione da quelli legati all'implementazione algoritmica e di sistema.

Capacità comunicative:
Capacità di comunicare e condividere le scelte di modellazione e i requisiti di sistema, nonché i risultati dell'analisi dell'efficienza degli algoritmi del mercato online.

Capacità di apprendimento:
Il corso stimola gli studenti ad acquisire capacità di apprendimento al crocevia tra informatica, economia e applicazioni del mercato digitale, compresi i diversi linguaggi utilizzati in questi campi.

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GENERAL
The Generative Models for Audio course aims to provide students with an in-depth understanding of the methodologies and techniques underlying generative models applied to audio signals. The course explores the main paradigms, including autoregressive models, latent variable models, normalizing flows, energy-based models, and diffusion models, emphasizing their theoretical foundations, probabilistic formulations, and computational implementations. Students will acquire conceptual tools to critically analyze the potential and limitations of different approaches, as well as practical skills to develop, train, and evaluate generative models on complex audio data. Special attention will be devoted to advanced applications such as speech synthesis, voice editing, audio inpainting, source separation, and audio compression. In addition, the course will address interdisciplinary topics related to unsupervised and self-supervised learning, as well as the interpretability and robustness of generative models. By the end of the course, students will be able to integrate theoretical and practical knowledge to tackle advanced problems of audio generation and manipulation, developing a critical and multidisciplinary perspective that is essential for research and development in data science and audio AI.

SPECIFIC
• Knowledge and understanding: Students will acquire advanced knowledge of generative modeling techniques for audio and will understand the theoretical and computational foundations of the main models.
• Applying knowledge and understanding: Students will be able to implement, train, and evaluate generative models on audio data, applying the acquired knowledge to practical and research problems.
• Making judgements: Students will develop the critical ability to assess the appropriateness and performance of generative models in different application contexts.
• Communication skills: Students will be able to clearly and effectively present the obtained results, explaining design choices and discussing the implications of the developed models.
• Learning skills: Students will acquire the ability to independently explore new models and methodologies in the field of audio generation, staying up to date with developments in the discipline.