Course-specific learning objectives

The Second-cycle Degree Course in Computer Science provides the graduate with in-depth theoretical, methodological, experimental and applicative skills in the fundamental areas of computer science.
The learning path outlines a highly qualified figure capable of studying problems, designing and developing innovative software systems, i.e. systems capable of learning, reasoning and interfacing with humans, in a natural, customised and proactive manner. The Second-cycle graduate will be able to plan, direct work and manage complex and innovative software systems. He/she will also be able to take on roles of responsibility in the analysis, design, development and maintenance aspects of software and information systems.

In addition to broadening and deepening basic knowledge of the cultural aspects of Computer Science, the Second-cycle degree course deals with methodologies for software development, with particular reference to its reliability and maintainability.
In particular, the course emphasises the following specific topics, which are of topical relevance and appreciated by stakeholders:
- research and development of innovative techniques for solving complex problems (Artificial Intelligence); - tools and techniques to support software quality control and verification.
Knowledge of the latest software methodologies and technologies, providing the tools to build innovative solutions, favours a rapid entry into the world of work both in the Information and Communication Technology (ICT) sector and in the various application sectors based on these technologies.
The introduction of the cultural approach necessary for the application of the scientific method in the field of computer science will also enable the best Second-cycle degree graduates in Computer Science to gain access to subsequent levels of university studies, such as a PhD programme or second-level professional master programmes.

The learning objectives includes:
- the completion and expansion of basic computer science and mathematics training;
- the acquisition and deepening of the elements of computational logic and statistical skills preparatory to the study and evaluation of methodologies for problem solving;
- the study of innovative methodologies and techniques for solving complex problems (Artificial Intelligence);
- the knowledge of intelligent systems based on machine learning and their impact on productive and social realities - the study of methodologies and techniques to support the quality control and verification of software and its development process;
- the practical deepening of the theoretical notions acquired through the performance of project activities, laboratories and/or training placements in companies, external bodies or internal laboratories of the University;
- activities to deepen the knowledge of the English language.
In order to improve the international usability of the knowledge and skills acquired, some of the courses may be taught in English.
The didactic regulations also establish appropriate mechanisms for encouraging study stays at foreign universities, within the framework of international agreements and within European or university programmes (e.g. Erasmus and Overworld).

Teaching methods

The training objectives outlined above are essentially achieved through the following teaching methods: face-to-face teaching, tutorials and laboratory activities.

A web platform ( Elly ) is available where the individual Professor/Instructor can enter his or her own teaching material (notes, exercises, insights) and set up self-assessment tests of learning during the course

Verification of learning outcomes

Each course unit provides for a final assessment, which may also be obtained by means of in-progress tests and/or a final project. The final assessment is normally expressed in thirtieths, with the exception of foreign language teaching and the curricular internship, which require a pass mark.

In the Syllabus of the individual subjects, both the teaching methods and the specific assessment methods are detailed.

Verification of the ability to synthesise and the degree of study autonomy in the face of new problems is assessed by means of a final examination, structured as described in the Course Regulations.