Data Science

Educational objectives

Educational goals

The aim of the course is to introduce students to the application of optimization techniques to training problems arising in supervised machine learning.

Knowledge and understanding

Students are expected to gain insight into the fundamental models used in supervised machine learning (Deep Networks and Support Vector Machines), as well as the most recent optimization algorithms for determining the parameters (training) of such models and the techniques for selecting the hyperparameters that best fit the available data.

Applying knowledge and understanding

By the end of the course, students should be able to select the most appropriate machine learning model for the problem at hand, use standard software to develop suitable models, and develop new optimization algorithms for training.

Making judgements

Lectures, practical exercises, and project sessions will provide students with the ability to assess the main strengths and weaknesses of different machine learning models applied to case studies and to select the models that best fit the application.

Communication skills

By the end of the course, students will be able to identify the main features of a machine learning problem and explain the techniques for its solution to both specialized and non-specialized audiences. These abilities are tested and evaluated through projects developed in small groups, thereby encouraging teamwork and fostering a proactive and collaborative learning process. These abilities may also be assessed during the final oral examination.

Lifelong learning skills

Students are expected to develop the learning skills necessary to undertake further studies on the relevant topics with a high degree of autonomy. During the classes, students are encouraged to work on projects in small groups, thereby stimulating active participation and engagement. They are encouraged to consult supplementary research publications and Internet resources to identify the techniques and detailed choices needed to accomplish the assigned tasks effectively. These skills are tested and evaluated through the final project reports, in which students discuss the main issues related to the problems addressed and justify the choices made to overcome the encountered difficulties, based on the topics and materials covered in class.

Educational objectives

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.

Educational objectives

Educational goals
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 for processing time series and images, such as frequency analysis, filtering, and sampling; (ii) Sparse and low-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:
Knowledge and understanding: Learn the basics of signal processing for machine learning and be able to apply these concepts to real data science problems.
Applying knowledge and understanding: Apply signal processing and machine learning techniques to real-world data sets, using programming languages such as Python and Matlab.
Making judgements: Analyze the benefits and limitations of different signal processing tools and models and determine the best methodology to use for a given data set.
Communication skills: Communicate effectively about signal processing for machine learning, including design constraints, solutions, and potential applications.
Learning skills: Develop studies in the field of signal processing for machine learning, including the ability to undertake research in this area.

Educational objectives

Educational goals

Knowledge and understanding
Knowledge and understanding of the main problems arising in high-dimensional data analysis and of relevant approaches

Applying knowledge and understanding
Ability to apply acquired knowledge and understanding to scenarios arising in the analysis of high-dimensional Euclidean or non-Euclidean spaces

Making judgements
Ability to critically judge and evaluate the effectiveness of proposed solutions

Communication skills
Ability to convey and explain reasoning underlying design and technical choices to solve scenarios of interest

Learning skills
Ability to track the evolution of core techniques taught in the course and learn new variants

Educational objectives

Educational goals
Knowledge and understanding
Knowledge and understanding of main problems arising in the analysis of graphs and their application areas.

Applying knowledge and understanding
Ability to apply acquired knowledge and understanding to scenarios arising in the use of graphs for the solutions of applied problems.

Making judgements
Ability to critically judge and evaluate effectiveness of proposed solutions

Communication skills
Ability to convey and explain reasons underlying design and technical choices to solve scenarios of interest

Learning skills
Ability to track the evolution of core techniques taught in the course and learn new variants

Educational objectives

General objectives:
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.

Specific objectives
The first part of the course includes delving into state-of-the-art DNN models for classification and regression applied to detection (where the objects are in the image), pose estimation (whether people stand, sit, or crunch), and re-identification (estimating a unique vector representation for each person). The course further discusses DNNs for multi-task objectives (joint detection, pose estimation, re-identification, segmentation, depth estimation, etc). This first part includes DNNs that apply to video sequences, by leveraging memory (e.g. LSTMs) or attention (Transformers).
The second part of the course delves into models, training techniques, and data manipulation for generalization, domain adaptation, and meta-learning. Further to transfer learning (how pre-trained models may be deployed for other tasks), it discusses multi-modal (with different sensor modalities such as depth or thermal cameras) and self-supervision (e.g. training the DNN model by solving jigsaw puzzles) to auto-annotate large amounts of data. Finally, it presents domain adaptation (e.g. apply daytime-detectors for night vision) and meta-learning, a most recent framework to learn how to learn a task, e.g. online or from little available data.

Knowledge and understanding:
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.

Apply knowledge and understanding:
At the end of the course, students will have become familiar with the most recent advances in machine learning across a variety of tasks, their adaptation to novel domains, and the continual self-learning of algorithms. They will be able to explain the algorithms and choose the most appropriate techniques for a given problem. They will be able to experiment with existing implementations and design and write programs for new solutions for a given task or problem in the two fields.

Critical and judgment skills:
Students will be able to analyze a problem or task and identify the most suitable methodologies and techniques to apply in terms of the effective resolution of the problem (accuracy) and its feasibility, including the efficiency, the required amount of data, and annotation. Further to class discussions, critical and judgemental skills will be the result of assignments, a course project, and a final project report.

Communication skills:
Students will acquire the ability to expose their knowledge in a clear and organized way, which will be verified through a final project presentation and its discussion.
Students will be able to express their solutions rigorously and explain the structure of the code they have written.

Learning ability:
The acquired knowledge will enable students to face the study of other problems in machine learning and computer vision. Learning ability will result from the chosen lecture topics, covering broad areas in advanced machine learning, as well as from the final project, for which students will deep dive into a new topic, beyond the thought material.

Educational objectives

Educational Goals

The course provides the theoretical, methodological, and quantitative tools required to analyze complex systems through the lens of Network Science. It introduces the main models used to represent, analyze, and interpret systems composed of a large number of interacting entities, with particular emphasis on the relationship between structure, collective dynamics, and empirical data. Students will acquire the skills needed to model complex phenomena, integrate empirical observations with theoretical models, and interpret processes such as diffusion, resilience, polarization, and opinion formation in social, technological, and information systems.

Knowledge and Understanding

By the end of the course, students will understand the fundamental concepts of network science and complex systems, including network generative models, dynamical processes on networks, and quantitative methodologies for studying collective phenomena. Students will understand how network structure shapes the emergence of macroscopic behaviors and will be able to critically interpret the results of complex systems analyses.

Applying Knowledge and Understanding

Students will be able to represent complex systems using network-based models, apply quantitative methodologies to relational data, evaluate structural and dynamical properties of networks, and use theoretical models to interpret empirical observations. They will also be able to formulate hypotheses, compare alternative modeling approaches, and apply analytical tools to social, technological, and information systems.

Making Judgements

Students will develop the ability to critically evaluate data, models, and empirical findings, recognizing their assumptions, limitations, and conditions of validity. They will be able to select appropriate methodologies for specific research questions and formulate independent, scientifically grounded interpretations of observed phenomena.
Communication Skills

Students will be able to communicate models, methods, and results using appropriate scientific language, effectively explaining complex concepts to both specialist and non-specialist audiences. They will also be able to present quantitative analyses and critically discuss empirical evidence.

Learning Skills

Students will acquire the conceptual and methodological tools required to independently explore advanced topics in Network Science, complex systems, and computational social science, as well as to undertake research activities and professional applications involving the quantitative analysis of complex systems.

Educational objectives

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.

Expected Learning Outcomes

At the end of the course, students will be able to:
Understand and describe the main wired and wireless technologies for Internet of Things (IoT) used in smart environments.
Analyze architectures and network protocols for the IoT and distributed sensing systems.
Design smart environment applications by integrating communication systems, edge computing, and machine learning algorithms.
Apply tools and methodologies for vehicular traffic monitoring and the implementation of low-power wireless networks.
Understand technologies and data processing workflows for multimedia and sensor-based applications in smart environments.
Design applications that incorporate extended reality (XR) content and assess their impact in urban and intelligent contexts.
Develop a critical and systemic approach to designing digital infrastructures for smart cities and environments, with attention to scalability, sustainability, and energy efficiency.

PROGRAM
The program is divided in two main parts:
PART 1
Module 1: Introduction to IoT Networking in Smart Environments
– Overview of wireless technologies in smart environments
– Enabling technologies and real-world applications
Module 2: Communication Solutions for IoT
– Low-power communication protocols: Zigbee, BLE, LoRaWAN
– 3GPP standards for IoT communication
Module 3: Vehicular Ad Hoc Networks (VANETs)
– Fundamentals of VANETs
– Communication challenges and solutions
Module 4: Applications & Big Data in Smart Environments
– Role of big data in wireless communication
– Practical applications and case studies
PART 2
Module 5: Signal Sampling Techniques
– 1D and 2D signal sampling methods
– Real-world applications of sampling
Module 6: Source Coding & Localization Applications
– Fundamentals of source coding
– Localization techniques and their applications
Module 7: Extended Reality (XR) Technologies
– XR principles and communication challenges
– XR applications in wireless environments
Module 8: Communication Architectures
– Architectures for modern wireless communication
– Integration of XR and IoT in communication networks

Educational objectives

Expected learning outcomes following the Dublin Descriptors
1) Knowledge and understanding
During the lessons of Data-driven Economics, students acquire the basic theoretical elements of econometric analysis. Theoretical lessons 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 lessons 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 of 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.

Educational objectives

Educational goals

Definition of the role of Big Data in the context of Official Statistics.
How to frame the measurement of social, demographic, and economic phenomena through Big Data, considering challenges, needs, and risks.

Knowledge and understanding

Knowledge of the main concepts required to operate in the field of Official Statistics.

Applying knowledge and understanding

Application of classic data mining methodologies to typical Official Statistics topics.

Making judgements

Ability to interpret the results of an Official Statistics analysis.

Communication skills

Ability to communicate results to an audience with non-technical skills.

Learning skills

Ability to quickly learn the specifics of the relevant Official Statistics domains.

Educational objectives

Educational 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.
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.
Making judgments: 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 skills: 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.

Educational objectives

General outcomes:
The course will present a broad survey of topics at the interface of computer
science, data science, and economics, emphasizing efficiency, robustness, and application to emerging online markets. It will introduce the principles of algorithmic game theory and mechanism design, algorithmic market design, as well as machine learning in games and markets. It will demonstrate applications to case studies in Web search and advertising, network economics, Data, cryptocurrency, and AI markets.

Specific outcomes:
Knowledge and understanding:
The algorithmic and mathematical economics principles underlying the design and the operation of efficient and robust online markets. The application of these principles in concrete examples of online markets.

Applying knowledge and understanding:
Being able to design and analyze algorithms for concrete online market applications with respect to the requirements of efficiency and robustness.

Making judgements:
Being able to evaluate the quality of an algorithm for online market applications, discriminating the modeling aspects from those related to algorithmic and system implementation.

Communication skills:
Ability to communicate and share the modeling choices and system requirements, as well as the results of the analysis of the efficiency of online market algorithms.

Learning skills:
The course stimulates the students to acquire learning skills at the crossroads of computer science, economics, and digital market applications, including the different languages used in these fields.