The course provides the basic competences in the field of digital signal processing, with special attention to images and video sequences. Starting from the fundamentals of 1D signal analysis and processing, analog and numerical, in time and frequency domains, we then extend the concepts to the multi-dimensional case of signals in space. Then, we introduce the more important approaches for image filtering and extraction of image descriptors. These concepts are further extended to deal with motion pictures. Finally, the problem of image compression is introduced, focusing on most known techniques for image and video coding, as well as their standard implementations. The approach of the course is rather practical, with the explanation of theoretical concept followed by their translation into algorithmic terms.

The course objective is to educate professional figures fully aware of the complex impact of AI and robotics on our society. In this respect, the student will be offered basic information on the ethical and legal implication of AI. Specifically, the course will expose the most important motives for a truly human centred AI and to the bioethical principles that could underly its construction. At the same time, the course will present and discuss the main regulatory tools that could be applied to AI at different levels (national regulations, European regulations, international regulations). The students will be interactively involved through the discussion of realistic cases and through the presentation of papers.

The course objective is to provide competencies on: i) the formalisms to specify planning and scheduling problems for an agent taking into account his/her capabilities and the expressivity of the planning objectives to achieve; ii) the different techniques to solve the defined problems; iii) the tools implementing the analyzed modeling and solving techniques.

The course provides the foundations of automated reasoning (AR). The main logic formalisms and algorithms for automated reasoning in the main domains of interest (e.g., SAT, SMT) are presented and illustrated by means of application. Lab sessions are provided where students will use state-of-the art AR systems.

Robots that can talk or chat with humans are becoming pervasive in many industry domains. AIS systems using natural language can interact with human and operate in command-and-control, information retrieval or cooperative decision-support tasks. In this course we review the basic principles of human computer interaction, conversation linguistics, discourse analysis, computational Dialogue Models, dialogue system architectures and their evaluation. In the second part of the course we provide methodologies for the design of Conversational Agents, data-driven training, design tools and a project-based lab addressing real use cases.

The goal of this course is to study two of the main paradigms of Bio-Inspired Artificial Intelligence, namely: Evolutionary Computation, inspired by evolutionary biology, and Swarm Intelligence, inspired by collective behaviors of social animals. First, the main theories and algorithms will be introduced. Then, it will be shown how these techniques can be applied, for instance, for solving complex optimization problems, training data-driven models, generating new contents (video-games, websites, art), finding bugs in software, automatically synthesizing or fixing computer programs, or finding innovative solutions in robotics, logistics, and engineering. Finally, it will be shown how these techniques can help the understanding of biological systems, in order to close the loop between biology and AI.

The course offers a bird-eye view on the most important topics related to modern robotics, highlighting the main problems that need to be faced in order to make the robots operate correctly in the environment. Specifically, the course will cover the following topics: taxonomy of the different types of robots, physical modelling (direct and inverse kinematics), simulation of models, sensing and actuation solutions for perception and action, motion planning. The course will adopt a combination of simulation models and lab experiences to consolidate the knowledge transmitted during the theoretical lessons.

The complexity of multi-agent systems has led to the definition of different development methodologies, software architectures and programming languages ​​in which the concept of agent assumes a key role as well as the concept of object-oriented object-oriented design. The objective of the course is to examine and explore the credentials of agent-based approaches as paradigm for the development of autonomous systems. We will present analysis and design techniques, architectures, algorithms, and tools for the development of agents. In particular, the course will deal with the agent concept and the development of multi-agent systems, architectures and algorithms for agent software design, planning principles, modeling techniques and goal-oriented analysis, communication languages ​​and agent-oriented software engineering methodologies.

The course aims to provide the student with an in-depth overview on the methods of analysis and management of multimedia data. Starting from the basics of image and video processing, the course will focus on the problems of motion modeling and detection, motion tracking, and object recognition, both by using monocular and multi-view systems.

The course aims to provide the students with the knowledge necessary to deal with complex problems in the field of computer vision. In particular, the course will give the students the theoretical and practical notions related to the main methods and algorithms for analyzing visual data based on neural networks and deep learning. The laboratory activity will complement the theoretical part of the course and will focus on the use of the main deep learning and computer vision libraries.

The course aims to make the students aware of the most recent developments in the area of Computer Vision. To this end, the course’s teachers and tutors will carry out an open discussion and presentation on the most relevant scientific papers, highlighting the theoretical foundations and practical implications through ad hoc laboratory experiences. The final exam will revolve around a scientific paper chosen in agreement with the teachers; the students will present the advantages and disadvantages of the technique presented in the paper, touching on such issues as the replicability of the results and the datasets. The discussion will follow the pattern of a scientific peer-review and the goal of the exercise is to deepen the scientific competences of the students and to strengthen their soft skills at the same time.

This course aims at presenting advanced topics on machine learning and optimisation research and technology. It will cover some of the most promising directions of recent research. The course includes lab exercises and seminars on selected topics.

The course topic is the application of AI tools to the perception of autonomous agents, with a particular emphasis on the distributed solutions (distributed perception and estimation). Bayesian and Markovian approaches will be introduced, together with Maximum Likelihood estimators, for classification, localization and motion predictions of the different entities acting in the operative scenario. The theoretical topic will be consolidated through simulations and lab experiments.

This course focuses on control of robotic systems, with special attention to numerical optimal control and reinforcement learning. After reviewing the basic principles of robot modeling and numerical optimization, students will learn different control techniques, from the simplest and most well-known, to the most recent and advanced. Methods will be first studied in theory, and then implemented in simulation (with the Python language) to gain practical experience. Applications will span industrial manipulators, legged robots, flying robots and wheeled robots. After completing the course, students will be able to: 1. understand the working principles of several control algorithms for robotic systems, 2. choose the appropriate approach(es) to control a specific system for a given target application, 3. implement, tune, and test control algorithms with the Python language

The course will delve into robotic deliberation, meaning the robots' ability to receive/decide a mission and to refine it into an executable and detailed tactical plan that fulfills its goals. More often than not, the latter amunts to deciding a trajectory that needs to be followed, avoding obstacles and cooperating with humans and other robots The student will receive a comprehensive introduction to the most important motion planning techniques and will refine her/his understanding through challenging laboratory experiences.

The course is designed for both Data Science and AI students and introduces the participant to the field of Behavioural Data Science. Behavioural data is a new, emerging, interdisciplinary field, which combines techniques from the behavioural sciences, such as psychology, economics, sociology, and business, with computational approaches from computer science, Artificial Intelligence, statistics, data-centric engineering, information systems research and mathematics, all in order to better model, understand and predict human behaviour. It emerges as a direct response to the need for studying behaviour “in the wild”, outside the “sterile” laboratory setting and controlled environments. And finally, it helps us create better prediction models and algorithms.

This course will focus on the generation of a Knowledge Graph (KG) starting from pre-exisiting data, usually available in Internet but also not publicly available (possibly, also personal data suitably anonymized). The main areas covered are: an introduction to KGs, a general methodology for KG generation, lexical resources, ontologies, and other types of datasets, tools and libraries as needed for dataset reuse and KG development.

The course introduces the topic of business design, a methodology for designing truly innovative products and services: desired by customers, economically sustainable, through an iterative and incremental methodology that allows you to test the main assumptions underlying the business model before launching on the market your product or service.

In the course, we will provide the basis for the application of AI techniques to product and process innovation.

The course will provide the student with fundamental knowledge on Microeconomy and business organisation, with particular regard to the economy of information, networks, and innovation in the areas of information and communication technologies and information systems. A particular attention will be devoted to decision making and management, and to the main factors affecting the decision of the stakeholders in companies, networks and markets. These principles have a primary importance both in the management of companies and in the definition of public policies for the regulation of markets.

Collaborative robots are robotic systems that operate in close connection with humans. Therefore, they have to comply with very challenging requirements in terms of safety, performance, and ergonomics. What is more, the interaction with humans for the execution of shared tasks demands high levels of flexibility and adaptability. In this context, it is not surprising that the software component plays a dominant role in the development of the system. To meet the challenging requirements listed above, the quality of the software component has to be of the greatest standards available in today's industrial practice.
In this course, the student will come into contact with the most recent technological advances in collaborative robotics. S/he will choose a project in the area with a level of complexity sufficient to justify the adoption of state-of-the-art programming techniques, but still manageable within the time-frame of the course. The specific theme of the project will be chosen in accordance with the interest of the group in one of the three macro areas: health, precision agriculture and manufacturing.
The student will learn:
1. advanced use of the C++ programming language,
2. use of the ROS2 programming framework,
3. how to design and develop modular, well-documented and tested code

This course provides basic concepts as well as design capabilities in the framework of sensing technologies and platforms, including remote sensing (from satellites, airborne, UAV and terrestrial observation platforms), proximal sensing, in-situ sensing and their possible integration with other kinds of ancillary data. These technologies are presented in the the framework of different applications including environmental monitoring, climate change analysis, civil protection, infrastructure monitoring, surveillance, planetary exploration, automotive, robotics, etc. The course introduces sensing principles, methodologies, technologies and techniques that are fundamental for the design of adavanced systems with the last generation of sensors (optical, multispectral, hyperspectral, thermal, radar, lidar, etc). It describes the approach to the design and implementation of systems with respect to different applications and operative scenarios, including the design criteria for the choice of the sensors and the the system architecture. A part of the course is focused the the data analysis methods that should be used for the processing of the data acquired by sensors (also in a data fusion frameowrk). A large part of the course is developed in the Sensing Technology Laboratory where the students can develop experiments on the use of most of the sensing technologies considered.

The course deepens one of the holy grails of AI: the development of computational models that ground language into vision. Its goal is to (1) provide students with an overview of the field showing how Computer Vision and Computational Linguistics have been connected to develop multimodal models; (2) deep the study of neural networks which understand and generate visually grounded natural language; (3) know evaluation methods of multimodal models as well as their limitations; (4) familiarize students with scientific papers and with the writing of scientific reports. At the end of the course, students will be able to (1) illustrate the main research directions in the field, the long-standing ones as well as the new challenges; (2) know the tools and resources at disposal and apply interdisciplinary approaches to the development of multimodal modal and; (3) compare different approaches with appropriate evaluation methods; (4) write a scientific report on a research project.

The course delves into the problem have to be addressed across the boundaries between meaning, persuasion, language understanding and cognitive social processes: why is so difficult to understand correctly, why is it difficult to lie. The course will illustrate how persuasion is generated and how this is related with stereotypes, with the natural difficulty in revisiting the knowledge acquired in the past, with the theory of negations and with how language modifies our behaviour.

The purpose of this course is to give the students the main concepts and basic methodologies for the study of language, divided according to the following thematic areas: phonetics, phonology, morphology, syntax, semantics, discourse. The main approach will be that of theoretical linguistics, in the generative framework. The course strives to stimulate an active and participative approach to learning and stimulate "problem solving". The students will have to use the skills acquired to solve various real Language problems, or do field work, using the languages spoken in class as a training field, in order to acquire a concrete understanding of what it means to carry out research in linguistics. Those students who already have a theoretical linguistic background will have an opportunity to strengthen their knowledge by doing more advanced exercises and activities, which will be planned together with the teacher.

"The goal of this course is to provide a good understanding of vehicle dynamics and control systems through a combination of classroom-based theory sessions, hands-on computer simulation and analysis of real data and example of recent advanced automotive applications. The aim of the course is teaching the student how to model vehicle systems and sub-systems in order to study/optimise vehicle dynamics and design control systems. Finally the course will provide the basic knowledge of intelligent vehicle architectures and the main techniques for trajectory plannings."

The course introduces some computational techniques for the study of:- Ordinary Differential Equations- Laplace and Z transform- Constrained maxima and minima- Optimal Control problems.

The objectives of the course are threefold: 1. Introducing the students to modern design of mechatronic systems, following the life cycle approach in its different phases: requirement definition, specification, conceptual design and simulation tools; 2. Providing the knowledge and the abilities required for the simulation of multibody and multidomain systems, which can be used for concept evaluation and for the generation of open loop models usable in control design, 3. Evaluating the accuracy of the model and the final result against the initial specification.

The course aims to give a first theoretical introduction, supported by concrete examples, to the use of mathematical optimization/operations research to solve problems and provide better solutions. It is ideally combined with a "Machine learning" course. A "Data Scientist" starts from rich and abundant data sources, builds mathematical models using the data (machine learning), presents and communicates the insights obtained, provides improved solutions (optimization). Innovation in industry and services is the final goal.

The course aims to study the fundamental aspects of general-purpose GPU programming. 
Students will be able to:
- analyze sequential algorithms by identifying performance bottlenecks;
- design parallel algorithms and implement them on GPUs by considering their architectural aspects;
- apply existing parallel and optimization techniques in a contest of different problems;
- manage and use tools and technologies to design efficient and scalable GPU code.
- design experimental setup and reporting results.

The course aims to provide the essential tools for the analysis of dynamical systems in the continuous and discrete time domain, from linear time-invariant systems, to time-varying ones and up to nonlinear hybrid systems.

The goal of this course is to provide students with the essential soft skills needed to excel in both technical and non-technical aspects of their careers. In this course students will have the opportunity to identify and develop their soft skills, focusing on both theory (readings and discussions) and on practice (exercises and simulations). These skills will support them in valorising their scientific competences.
The main soft skills covered in this course will be: Group Work and Collaboration; Effective communication and active listening; Problem solving; Constructive feedback and proactivity; Time management; Responsibilities and stress management.

The course will be on basic neural anatomy and on the application to neurocognitive sciences of such neuroimaging techniques as functional and structural magnetic resonance imaging, transcranial magnetic stimulation, Magnetoencephalography, and EEG. At the end of the course, the students will have the following abilities related to brain imaging: 1. Defining the right imaging method for each cortical source, 2. Identifying the best equipment to use, 3. Explaining advantages and liitations of each technique, 4. describing the best methods for data analysis, 5. Describing and interpreting neuroimaging applications to neuroscientific research.

The course deals with themes, problems and methods related to the current research on formal semantics of the natural language. The course will also highlight the main differences between model theoretic semantics and approaches such as embodied mental representations and corpus-based distributional semantics. The course is a natural follow-up of the course “Introduction to human language” and complements its topics. At the end of the course, the students will have the following competences: 1 basic understanding of formal logic (propositional and predicate calculus), 2. computing the truth of logic formulae using truth table and refutation trees, 3. Evaluating the semantic value of a formula in a formal model, 4. Translating natural language quantified statements (in English or Italian) into extended predicate logic, 5. Using lambda calculus to obtain the truth value of complex expression in natural language from the truth value of its constituents, 6. Formulating the problem of the semantic coverage of advanced natural language phenomena such as ambiguity in quantification, event semantics, the correct expression of plurality, the distinction between mass and count terms and between distributive, collective, and cumulative predicates 7. Ability to read a scientific paper through formal semantics extracting at least the most salient points.

The course is intended to provide the fundamentals of High Performance Computing software for High-end Data Science. The course will include both theoretical and practical aspects related to simulation-centric and data-centric paradigms; it will present convergent aspects of HPC software ecosystems and data frameworks at a large-scale in relevant scientific disciplines.

The course aims to extend students’ knowledge of grammatical, lexical and textual features of written academic English in a scientific context, and to develop students’ fluency in speaking English. An active approach is used, with students producing written texts and then correcting them individually and as a group.

This course aims to introduce the student to the latest developments in ultrasound-based medical technologies. Theoretical lessons complemented by laboratory sessions will allow students to have a broad understanding of these technologies and their application in a clinical context, ranging from aspects of the interaction between ultrasound and biological tissues to the limits and capabilities of the data acquisition and analysis systems and their ability to provide clinically relevant information.

The course aims to introduce students to the use of computational techniques and digital data sources for studying the determinants of human health, particularly in epidemiology.
At the end of the course, students will be able to:
▪ know the fundamental principles of medical and epidemiological statistics;
▪ analyze heterogeneous data sources (social media, mobile telephony, search
engines) from which to extract relevant indicators for public health;
▪ know the main approaches of computational epidemiology (surveillance passive,
mathematical modeling, sensor data)
▪ develop numerical models to describe the spread of infectious diseases on
different spatial scales.

The overall goal of the module is to provide a theoretical and practical basis for the design, development, and evaluation of interactive systems, which are multisensory, tangible, and networked. Students on this module will have the opportunity to explore physical computing concepts, use real-time programming languages, understand the bases of human sensory perception, and design networked interactions in both co-located and remote settings.

The course provides an introduction to state of the art methods in Natural Language Processing (NLP) and their applications. In particular, the focus of the course is on deep learning methods, foundational and large language models. The course will follow a mixed theoretical-practical approach, including theoretical and practical sessions, along with seminars on recent and relevant research works. The students are expected, after completion of the course, to have gained both theoretical and practical understanding of current NLP methods.

This course focuses on designing and building AI-powered systems at scale, emphasizing real customer needs. With a startup mindset, students will learn how to architect, prototype, and iterate on AI-driven solutions that align with business goals and user expectations. The course covers system architecture, AI/ML integration, scalability, user experience, product-market fit, and system modularity. It also emphasizes system testing, evaluation, service-oriented architectures, API design, and modularization. By the end of the course, students will have designed a complete AI system, from ideation to deployment, and present it to investors, customers or stakeholders.

The course is designed for both Data Science and AI students and introduces the participant to the field of Behavioural Data Science. Behavioural data is a new, emerging, interdisciplinary field, which combines techniques from the behavioural sciences, such as psychology, economics, sociology, and business, with computational approaches from computer science, Artificial Intelligence, statistics, data-centric engineering, information systems research and mathematics, all in order to better model, understand and predict human behaviour. It emerges as a direct response to the need for studying behaviour “in the wild”, outside the “sterile” laboratory setting and controlled environments. And finally, it helps us create better prediction models and algorithms.

This course is aimed at providing the students with foundational knowledge on the design and the development of systems enabling users to interact with machines (computers, robots, virtual agents) by means of intuitive, everyday human behavior. These systems exploit the richness of the human capability to interact with the world through a single or multiple modalities together, going beyond the interaction paradigms traditionally adopted in Human Computer Interaction.

The goal of the Research Project is to involve the student in the development of a research project to be defined with a DISI professor/lecturer and possibly integrated in a broader research activity involving a group of other people (students or not). The students will acquire 1) specific technical and research competences and skills exercised during the work on the project and 2) soft skills necessary for the project activity such as working goal-oriented, reporting of partial and final results, passing periodical reviews and so on.