The AI@DEI Master’s Pathway at the Department of Information Engineering of the University of Padua is a cross-disciplinary curriculum dedicated to Artificial Intelligence, designed to integrate advanced AI competences across the different fields of information engineering.
It is a shared pathway within the Master’s degrees in Bioengineering, Computer Engineering, Control System Engineering, ICT for Internet and Multimedia, and Electronic Engineering, with the aim of providing a synergistic and interdisciplinary perspective on AI. Artificial Intelligence is addressed in its various application domains, from embedded systems and robotics to digital healthcare, multimedia systems, and networked infrastructures.
The structure includes common foundational courses, ensuring solid theoretical knowledge and shared methodological tools, followed by specialized tracks embedded within each Master’s degree. This approach allows every student to shape a coherent academic profile aligned with their chosen program, while maintaining a broad and up-to-date understanding of intelligent technologies.
The pathway also offers the opportunity to earn micro-credentials (academic certifications that formally recognize advanced and targeted competences in specific areas of Artificial Intelligence), as an additional and cross-cutting specialization. Micro-credentials enable students to enhance their academic journey beyond the 120 ECTS credits required for graduation, strengthening their professional profile with clearly identifiable and internationally relevant skills.
The common foundational courses include:
Foundations of Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, and Digital Circuits for Neural Networks (elective).
AI@DEI is designed for students who aim to develop intelligent systems, interpret complex data, and contribute to the evolution of technologies that are reshaping industry, healthcare, communication, and society.
Possible Micro-credentials
Control Systems Engineering
Industrial and edge machine-learning
Visual-Language-Action models for robotics
Bioingegneria
Biomedical wearable technologies for healthcare and wellbeing
Decision Support System for healthcare
Computer Electronics
Agentic Artificial Intelligence for Information Access
Learning from Networks
ICT for Internet and Multimedia
Digital Forensics and Biometrics
Machine Learning for Human Data
