Admission to the Master's Degree Course in Artificial Intelligence Engineering requires possession of one of the following qualifications obtained at an Italian University, or another qualification obtained abroad and deemed equivalent to them: Laurea or three-year University Diploma, Laurea Specialistica or Laurea Magistrale, pursuant to DM 509/1999 or DM 270/2004, five-year degree (prior to DM 509/1999). The knowledge required for admission is, in addition to that relating to the basic subjects (Mathematics, Physics, Computer Science) typical of Engineering, that characterising Artificial Intelligence with particular reference to knowledge typical of Information Processing Systems. It is also required that the student has a basic knowledge also in the broader area of Information Engineering and therefore, in particular, has a basic knowledge of Electronics, Telecommunications and Automatic Controls. In particular, for candidates with an Italian qualification, the curricular requirements for admission will be met by the possession of 90 CFU, acquired in any university course, in the MAT/xx, FIS/xx, INF/01, ING-INF/xx and L-LIN/12 sectors. The distribution of CFUs between the sectors is detailed in the course regulations. The curricular requirements of candidates with foreign qualifications necessary for admission will be assessed by a commission appointed by the Course Council through an analysis of the curriculum of studies submitted. A special Commission assesses the need for any curricular integrations, providing, in the case of courses that are not perfectly consistent with the requirements, an integrative course that must in any case be completed before the personal preparation test. Verification of personal preparation is compulsory for enrolment on the course and is carried out by means of verification of the degree grade or the weighted average of grades from the previous career, as described in detail in the course's didactic regulations. Knowledge of English at a level not lower than B2 of the Common European Framework of Reference for Languages will be required when verifying personal preparation.

The Didactic Regulations of the Course of Study indicate the required level of knowledge of the Italian language and the relevant test methods. Students may include in their study plan activities aimed at achieving these language skills.

Students must possess the following curriculum requirements beforehand: at least 90 CFUs (university credits) obtained overall with a minimum number of CFUs for each SDS obtained in the following groups:
- MAT/xx, FIS/xx = 30 ECTS credits
- INF/01, ING-INF/xx = 57 ECTS credits (of which INF/01 + ING-INF/05 >=18)
- L-LIN/12 = 3 ECTS credits
The preparation is also considered adequate if the student has obtained a degree mark of 85/110 or higher.
In the case of a foreign qualification, the final mark must be higher than 3/4 of the maximum prescribed or, failing that, the weighted average of the marks must be higher than 3/4 of the maximum prescribed.

Expert in Artificial Intelligence
The Artificial Intelligence expert possesses the skills to plan and implement innovation and product development projects in the field of Computer Engineering and in particular in the field of Artificial Intelligence, starting from the definition of specifications, through to design, definition of production and service tools and technologies, testing and certification. He/she also possesses skills to operate in ever-changing production and service sectors that require a high degree of specialisation in artificial intelligence methods and tools, thus being able to deal with the design, implementation, adaptation and management of highly innovative products and services. Finally, he/she possesses the skills to move in interdisciplinary contexts and to foster innovation in the working environment, whether in the company's operational sectors or in research and development centres, and to provide his/her expertise to support the technical and commercial structures of companies operating in the artificial intelligence or related fields.
Competencies can thus be stated in brief as:
1) Identification, formulation, resolution of complex problems requiring artificial intelligence approaches in different application areas, including interdisciplinary ones;
2) application of the main knowledge representation and management techniques, the main and latest programming languages and environments, the main algorithmic techniques for AI and combinatorial optimisation for the design and implementation of artificial intelligence systems
3) application of the main techniques of data mining, machine learning and deep learning as well as the main libraries existing in these fields for the design and implementation of artificial intelligence systems with applications in the industrial, health and biological fields
4) application of the main computer vision and multimedia data processing techniques
5) use of basic knowledge of the computational aspects of intelligent systems
6) application of the main protocols and analysis techniques for managing Internet of Things sensors, including in distributed contexts
7) knowledge of the main platforms and software solutions for managing intelligent robotic systems

Expert in Artificial Intelligence
The Artificial Intelligence expert holds high-profile scientific, technical and/or managerial positions in contexts that require in-depth knowledge of Computer Engineering disciplines with particular reference to artificial intelligence-based systems. S/he can operate in the fields of research, design, development, engineering, production, innovation, operation and maintenance, management of artificial intelligence solutions and technologies, and their utilisation in areas ranging from automation of complex business processes, mobility, citizen service management, finance, health and the environment.
In summary, the functions concern:
1) implementation of planning and optimisation systems
2) implementation of automated learning systems
3) implementation of decision support systems
4) implementation of machine vision systems
5) implementations of systems for health data analysis and bioinformatics
6) implementation of systems for industrial automation and robotics

Expert in Artificial Intelligence
Thanks to a training offering that emphasises significant laboratory activity in various application and industrial domains, the typical occupational outlets of the Artificial Intelligence expert are relevant to both corporate operational sectors and research and development centres, in particular:
- companies in the design, development, engineering, production and operation of intelligent solutions and systems and their applications;
- manufacturing companies, agri-food companies, civil engineering companies, public administration sectors and service companies in which computer systems based on artificial intelligence are used;
- companies involved in the acquisition, processing and transmission of information (data, voice, images and video);
- automation and robotics industries, manufacturing companies using process automation systems and plants;
- companies active in the design and development of embedded systems and digital platforms for autonomous and intelligent systems;
- companies from different sectors that need expertise in the development and use of artificial intelligence-based systems to support internal organisation, production and marketing;
- companies in the biomedical sector, requiring expertise in medical imaging, bioinformatics and data analysis with machine learning and artificial intelligence technologies;
- research and development centres, both public and private;
- third-cycle studies and advanced master programmes.

The Master's Degree Programme in Artificial Intelligence Engineering aims to provide skills related to the design, implementation and management of intelligent systems based on the latest artificial intelligence methodologies and techniques, thus capable of efficiently processing and extracting useful knowledge from large amounts of data. The objectives of the Master's degree include learning the theoretical foundations, methodologies and technologies capable of enabling the development of projects and the realisation of products characterised by strong innovation and appropriateness, in order to cope with the rapid evolution that characterises the area of Artificial Intelligence. In general, master graduates in Artificial Intelligence Engineering are required to have an in-depth knowledge of the theoretical and scientific aspects of the basic sciences and mainly of the Computer Engineering applied to AI to interpret, describe and resolve also in an innovative way the complex issues of the Engineering that may also request an interdisciplinary approach. In addition, they must be able to design, plan, project and manage learning systems, complex and/or innovative processes and services, also taking into account the economic, social, and ethical implications associated with them;
The aim of the Master's degree in Artificial Intelligence Engineering is to offer a balance between generalist training, albeit at an advanced level, in the various theoretical and technological fields in which the discipline of artificial intelligence is structured, and specialisation in one of the discipline's application areas. The aim is therefore to offer a “T-shaped training”: alongside a set of skills in one of the specific areas of Artificial Intelligence (which make up the vertical bar of the T), a set of skills, again characterising the subject, with a broad and foundational profile (which make up the horizontal bar of the letter) are offered. We believe that training of this kind provides the skills to be immediately active in the world of work, but also capable of keeping up-to-date when innovations brought about by research make acquired operational knowledge obsolete.
The Master's Degree Programme in Artificial Intelligence Engineering envisages a body of core courses in the following learning areas: artificial intelligence and machine learning, IOT, computer vision, multimedia, distributed applications, high-performance computing and robotics.
In addition to the core subjects of the programme, the Master's Degree Programme in Artificial Intelligence Engineering gives students the opportunity of set their educational path according to their professional aspirations, making choices that may lead the future Master's graduate to better complete their preparation. In order to complete their preparation, students will have to identify further teachings, according to training programmes that are functional to the achievement of the programme's training objectives, ensuring that both related and supplementary training activities and those freely chosen by the student must be consistent with this training project. These choices will therefore cover scientific subjects, other engineering subjects and legal subjects, such as operations research, discrete mathematics, the mathematical foundations of machine learning, telecommunications, law applied to artificial intelligence, and quantum computing. Therefore, in order to reach the training objectives listed above, the Master’s Degree Programme in Artificial Intelligence Engineering offers a sound cultural and methodological training programme that can be integrated with customised study paths aimed at providing a type of training focusing on both the access to the job market and the continuation of studies in advanced master programmes and/or PhDs.
The English language is the standard used in computer communication. Master graduates shall be able to promote technological innovation, and to that purpose they shall be fluent, both in writing and orally, in English other than Italian, with reference also to disciplinary lexicons. It is through the provision of teaching in English and the selection of English as the language of the programme that we intend to promote and consolidate this competence in the graduate.

The communication skills that are required of a future master's graduate in Artificial Intelligence Engineering relate in particular to the ability to:
- interact effectively with both non-specialists and specialists from different application areas of artificial intelligence in order to understand their specific requirements for the realisation of complex machine learning systems;
- describing in a clear and understandable way the information, ideas, issues and solutions to them, other than technical aspects;
- training collaborators, coordinating and participating in project groups, planning and carrying out training in the area of Artificial Intelligence;
- discuss about the topics of interest in an effective and fluent manner, both in writing and orally, in English and in Italian, with reference also to disciplinary lexicon, and using multimedia tools where required.
Such abilities (in English) are ascertained both through written and/or oral examinations provided in the single teachings, and by taking up an internship or project activity, and in writing and presentation of the Master’s Degree thesis during the final examination. Communication skills can also be put to the test in various ways: by taking some exams abroad thanks to the possibilities offered by the Erasmus Programme and by carrying out an internship/project activity abroad and then writing the Master's thesis in English.

Future Master graduates in Artificial Intelligence Engineering are required to:
- autonomy of judgement in analysing and designing complex systems for automated learning, evaluating the impact of IT solutions in the application context of AI, both in application terms, and relating to the technical and organisational aspects, and proving the active participation in the decision-making process in contexts that may also be interdisciplinary.
- autonomy of judgement in assessing the economic, social and ethical effects related to the AI solutions identified.
The Master’s Degree Programme in Artificial Intelligence Engineering is aimed at providing students with the suitable methodological and operational tools useful to independently and objectively deal with both the typical issues relating to the design and realisation of complex autonomous systems capable of learning, and the innovative challenges arising from the rapid evolution in the area of Artificial Intelligence.
The assessment of the expected results is carried out both in the single teachings, including laboratory activities, both in taking up an internship or a project activity, and in the final examination.

Future Master graduates in Artificial Intelligence Engineering are required to:
- learning ability to deal effectively with changing work issues related to innovation in the area of competence.
- the ability to recognise the need for independent learning throughout their life, given the high rate of technological and methodological innovation in the Artificial Intelligence area;
- the ability to independently gain new specialist knowledge from the scientific and technical literature of the sector, both within the topics further explored in their training path, and in other fields of machine learning;
- in-depth learning ability that are necessary to take on both subsequent studies as university advanced master programmes and/or PhDs, and scientific research.
Such abilities are assessed within the individual teachings, in particular those including a seminar component, of bibliographical research and development of both individual and group projects, other than in the development of activities relating to an internship or project activity and in the preparation and discussion of the master’s degree thesis.

Fundamentals (common to both curricula)
- Know and understand the main pattern recognition and machine learning techniques for the analysis of heterogeneous data.
- Know and understand computer vision techniques, in particular for image and video processing and cognitive systems in general with reference to processing, multisensory systems, including from IoT, with reference to cognitive robotic systems.


Applications curriculum
- Know and understand the advanced features of the Internet of Things and the management and analysis of such sensors also in distributed contexts.
- Know and understand the basicand advanced methodologies in the state space for the analysis and control of dynamic systems for robotic applications.
- In-depth knowledge and understanding of bioinformatics concepts and technologies


Large Scale curriculum
- Know and understand multimedia data compression and management techniques, with particular reference to storage standards for images, video and audio, and related architectural media.
- Know and understand the main models and technologies for managing advanced distributed software systems, also in terms of data collection by IoT sensors.
- Know and understand the problems and structure of multithreaded systems and clusters for high performance computing.


Preparatory knowledge for professional skills
- Know and understand the essential mathematic fundamentals on discrete sets, highlighting the resolution and demonstration techniques related to their study.
- Know and understand the technologies, the interconnection devices and the main network infrastructures, also in the automotive sector.
- Know and understand the essential mathematic concepts that govern the systems and algorithms of automatic learning.
- To know and understand the concepts behind quantum information processing.
- Know and understand the methodologies for person-centred design and interaction design.

Fundamentals (common to both curricula)
- Be able to apply and design, with the latest languages, libraries and programming environments, the main algorithms for classifying data, temporal sequences of information and complex patterns such as images and other multimedia data.
- Know how to apply the main techniques of both supervised and unsupervised machine learning, knowledge representation and management.
- Know how to apply basic techniques for the automatic analysis of multimedia data, such as images, videos and visual representations of data; know how to understand a scene, researching its contents; knowing how to deal with 3D vision.
- Know how to develop robotic and industrial vision applications, video surveillance biometrics and forensic analysis, artificial intelligence systems in both industrial, health and biological fields.
- Know how to use basic approaches to the computational aspects of intelligent systems


Applications curriculum
- Know how to analyse and design systems that are able to communicate with the new smart devices.
- Know how to analyse and design linear, non-linear, continuous, discrete, SISO (single-input and single-output) and MIMO (multiple-input and multiple-output) dynamic systems for robotic applications.
- Know how to build statistical models for interpreting data from molecular biology and biochemistry experiments, apply mathematical tools for analysing DNA, RNA and protein sequences, and optimise data search algorithms to improve data accessibility.


Large Scale curriculum
- Be able to apply and change the algorithms for compressing and processing multimedia data.
- Be able to design client-server systems based on objects, self-contained systems (agents) and apply the technologies for mobility.
- Know how to run simulations and use software libraries on systems with numerous GPUs and know job management methodologies on large HPC facilities.
- Being able to design systems for distributed, large-scale training of reinforcement learning models in a simulated environment for the navigation of autonomous robots.


Preparatory knowledge for professional skills
- Be able to apply the concepts relating to equivalence relations, prime numbers, issues of factorisation and modular arithmetic, and apply the main resolution techniques of recursive relations and the basic elements of the graph theory.
- Be able to apply the concepts relating to the main network infrastructures: at local, metropolitan and geographical level, in optical and electronic and technology, as well as V2X communications.
- Be able to apply the concepts relating to varieties, matrix factorisation, immersions in highly dimensional spaces, use of kernels in support vector machines.
- Being able to decipher, create and use simple quantum algorithms and knowing a technology platform for quantum computing and its hardware realisation.
- Know how to use innovative tools for generating virtual prototypes in order to evaluate human-machine and human-computer interaction using digital approaches.