Master's degree in Artificial intelligence

The Master's Degree in Artificial Intelligence aims to train specialists in the field of computer science (Master's Degree Class LM18) capable of developing tools, mostly software, based on the techniques and methodologies specific to Artificial Intelligence (AI). It is also widely recognized how these technologies are being characterized in the landscape of computer science and information technology as a unified corpus for methodologies employed (automatic reasoning and machine learning) and represent one of the most developing areas in the field of STEM sciences.

The main educational objective is to acquire methods and tools to develop fundamental techniques of artificial intelligence, and to design agents capable of autonomously acquiring knowledge and developing models and strategies. Possible applications include areas such as: ecology (e.g., environmental monitoring, climate change), economics (e.g., finance and insurance markets), sustainable growth (e.g., smart building, smart cities, smart grids), medicine (e.g., diagnostics, tele-medicine), product and service industries (e.g., AI-assisted programming, automatic text and speech translation, social signal processing, cybersecurity, autonomous robots, assisted and autonomous driving systems), gaming and entertainment, intelligent, social and human-centered user interfaces (Human Centered Design).

With this in mind, the course places special emphasis on issues related to the development of methods and technologies, mainly software, of artificial intelligence that are secure, reliable, fair, interpretable, i.e., capable of explaining one's decisions, according to the new paradigm of XAI ("eXplainable Artificial Intelligence"). In relation to these educational objectives, the Master's Degree Course in Artificial Intelligence proposes teachings aimed at obtaining specific skills mainly in the fields of computer science and information engineering, with an emphasis on methods and tools to develop artificial intelligence techniques, with particular reference to machine learning, intelligent agents, automatic reasoning, computer vision, knowledge representation, planning, game theory, natural language processing, logic, and the history of artificial intelligence. These teachings will be complemented by learning paths that will enable students to develop knowledge on: analytical/quantitative tools, such as methods and models of probability calculus and physics, inferential statistics, optimization techniques, and decision theory; epistemological and philosophical aspects underlying computational thinking; ethical foundations for the management of artificial intelligence technologies; legal aspects and principles concerning the regulation for the management of artificial intelligence-based technologies; and tools for the development of applications in economics and finance.