University of Florence

What you'll study

This two-year Laurea Magistrale (Master’s) combines core foundations in mathematics and computer science with specialised AI topics. The programme typically comprises taught modules, laboratory work, a substantial research project or thesis and opportunities for internships. Students follow a coherent curriculum designed to reach 120 ECTS credits over the course.

• Core foundations: advanced linear algebra, probability and statistics for data science, optimisation methods and numerical computing.
• Machine learning and deep learning: supervised and unsupervised learning, neural networks, convolutional and recurrent architectures, model selection and evaluation.
• Knowledge representation and reasoning: logic-based AI, semantic technologies, ontologies and symbolic approaches to problem solving.
• Probabilistic models and statistical learning: Bayesian networks, graphical models, hidden Markov models and probabilistic inference.
• Natural language processing (NLP): language modelling, parsing, text mining and advanced techniques for understanding and generating human language.
• Computer vision and perception: image processing, feature extraction, object recognition and vision-based learning.
• Robotics and autonomous systems: perception–action loops, planning, control basics and integration of AI algorithms in robotic platforms.
• Data engineering and big data: data pipelines, scalable learning, databases and practical issues of deployment and productionisation.
• Ethics, privacy and legal aspects: societal impact of AI, fairness, accountability, security and data protection considerations.
• Practical labs and project work: hands-on programming, frameworks (Python, TensorFlow/PyTorch), team projects and a final master’s thesis or applied project carried out in a research group or industry placement.

Entry requirements

Applicants are normally expected to hold a recognised first-cycle degree in computer science, information engineering, engineering, mathematics, physics or a closely related discipline, providing solid grounding in programming, discrete mathematics, linear algebra and probability. Candidates with degrees in other quantitative fields may be considered if they demonstrate equivalent preparation through prior coursework or professional experience.

Selection may be based on academic transcripts, a statement of purpose and, in some cases, an interview or qualifying assessment to verify mathematical and programming readiness. International applicants must show sufficient language competence: proficiency in Italian if the programme is delivered in Italian, or proficiency in English if courses or tracks are offered in English; evidence is typically requested at application.

Career prospects

Graduates from the programme are prepared for a variety of technical and research-oriented roles across industry and academia. Typical career paths include:

• Machine Learning Engineer or AI Engineer, designing and deploying learning systems.
• Data Scientist or Applied Data Analyst, extracting insights from complex datasets.
• Computer Vision or NLP Specialist, building perception and language systems for commercial products.
• Robotics Engineer, working on perception, planning and control for autonomous platforms.
• R&D Scientist or Research Associate in universities, public research institutes or private labs.
• Technical consultant or AI product developer in start-ups and established technology firms.

Graduates who wish to pursue further research commonly continue to doctoral programmes or join research teams, benefiting from the strong academic research environment in Florence and regional research centres.

Why study at University of Florence

The University of Florence offers studies in AI within a long-established, research-active university that combines fundamental theoretical training with applied laboratory experience. Students benefit from connections with multiple departments and research centres, access to computing resources and collaborative projects with regional industry and public research institutes. The city’s academic community supports interdisciplinary work across computer science, engineering, cognitive science and ethics, and students can take advantage of mobility programmes and internship opportunities within Tuscany’s growing technology and innovation networks.