The Bachelor’s Degree Program in Applied Computer Science and Artificial Intelligence aims to train professionals capable of successfully tackling—also at the international level—the challenges posed by the growing demands of the information society, particularly in the areas of data processing and decision-making based on artificial intelligence and machine learning.
Graduates of this program are computer scientists with a solid foundational education in the core areas of computer science, including programming languages, software design and development methods, algorithms and computational complexity, discrete structures, and the theoretical foundations of computer science.
This background provides them with the methodological tools necessary for continuous knowledge development throughout their careers, enabling them to keep pace with the rapid evolution of information and communication technologies.
In addition, graduates will have strong technical competencies, particularly in areas such as parallel and distributed software, computer architectures, computer networks, information systems and databases, human-computer interaction, artificial intelligence, and cybersecurity—thus enabling quick integration into the workforce in the field of digital technologies and intelligent systems.
Graduates will also be qualified to pursue second-cycle (Master’s) degree programs in computer science or in related scientific fields.
Their foundational academic preparation will ensure that graduates develop:
- familiarity with the scientific method and its ethical implications;
- a solid understanding of basic mathematics and the ability to apply mathematical tools in support of computer science;
- methodological knowledge and core competences across a wide spectrum of fields in information and communication technologies and artificial intelligence, including big data techniques and data-driven learning;
- familiarity with English, the primary language used in scientific and technical contexts, enabling them to study the subject matter directly in the language and facilitating access to the international job market.
Thanks to their solid theoretical, methodological, and technological foundations, graduates will be able to:
- understand the technological evolution of computer science, including both methodological and technological developments, and adapt to the continuous advancement of the field by consulting advanced scientific and technical documentation;
- independently build models essential for the understanding and formalization of complex problems in artificial intelligence;
- work in the design, development, and management of intelligent digital systems, information systems, and parallel computing on local and distributed networks, as well as in cybersecurity solutions;
- provide technological support to users of computing and artificial intelligence systems;
- enter the workforce quickly and effectively, bringing both interpersonal and decision-making skills and the ability to work both independently and as part of a team;
- communicate and justify their ideas regarding both problems and proposed solutions to specialist and non-specialist audiences, both nationally and internationally;
- engage in meaningful dialogue with end-users and domain experts and apply their knowledge to real-world situations in business and institutional settings;
- understand the economic, legal, ethical, social, and environmental implications of the digital transformation;
- pursue graduate-level studies in computer science and related fields.
Graduates will be able to work professionally in both software-producing and software-using organizations, in the public and private sectors, in the following occupational areas:
- design, organization, maintenance (including evolutionary maintenance), and management of software systems, application software, databases, information systems, and decision-making and predictive systems based on artificial intelligence;
- design, organization, and evolutionary maintenance of security and reliability components of IT systems, including data security and learned intelligence in machine learning contexts.
Program Structure
The curriculum is structured as follows:
- First year: students acquire foundational knowledge in mathematics and physics, along with an introduction to core concepts in computer science;
- Second year: the mathematical foundations are completed and further knowledge in computer science is developed, with a focus on modeling, optimization, and AI software learning techniques;
- Third year: the computer science education is completed through courses in applied computer science and artificial intelligence, allowing students to specialize in more theoretical or applied areas depending on their interests.
The third year also includes elective credits, a mandatory internship, and the final graduation exam.
All courses include laboratory activities, design projects, or problem-solving exercises.
In particular:
- nearly all computer science courses involve hands-on lab work;
- mathematics and theoretical courses include problem-solving sessions;
- lab activities in the first year focus on developing simple programs, while in the second and third years they evolve into design-oriented labs, focusing on applied computing, algorithms, and AI frameworks.
Internship and Final Examination
The internship is carried out under the supervision of a faculty advisor and may take place:
- externally, in companies or public/private institutions with which the university has agreements, or
- internally, within the degree program itself under faculty supervision.
In both cases, the internship involves working on a real-world problem, which the student must solve by developing a project using a professional approach, typically involving analysis, design, and software development activities.
The final examination consists of a written report presenting the results of the internship and an oral presentation, in which students demonstrate their understanding of the topics addressed and the tools used.
Teaching Regulations
The program regulations define, in accordance with applicable laws, the proportion of the total workload dedicated to individual study or other independent learning activities required of students.