All Courses

AS-AI aims to train the next generation of interdisciplinary researchers, professionals and leaders to address their challenging problems by the application of the powerful means of computational modelling (so as they are in particular developed within the ML, CEN e CSS approaches).

AS-AI students come to the School with diverse academic and professional backgrounds (ICT, engineering, psychology, economics, social sciences, biology, health, humanities) and are stimulated to pioneer innovative, interdisciplinary approaches to yield new insights and finding innovative solutions in the field of interest.

This unique program is designed to be in-depth yet flexible: students are offered FUNDAMENTAL COURSES, SPECIALISTIC COURSES, FOCUSSED COURSES, seminars and activities, with the aim to provide both a deep knowledge and specialisation and a broad view and transversal competences, skills and perspectives.


These courses, for the cross-cutting relevance of their subjects, are common to all the three School Tracks (MLA, CEN e CSS) and aim at reinforcing and deepening the understanding of the science, technology and engineering that underlies current computational modelling. These courses aim at giving both Theory (founding knowledge and tools) and Competences (capacity to employ the acquired knowledge/tools to concretely solve own specific problems). This division gives the instruments to meet the different needs of students coming to the School from different academic backgrounds: for example, a student with a degree in Psychology/Philosophy/Biology following a course on Programming and having limited preparation on the topic, will follow the theory part of the course in much detail; whereas, a student in Engineering/Computer Science will directly focus on Competences by applying the techniques of the course to sub-problems of his own Project.

Elements of linear algebra for AI Cecconi
Fundamentals of statistics for AI with R software Paolucci/Cecconi
Python programming for AI (including Git, NumPy, Pandas, MatPlotLib)  Mannella
Elements of numerical optimization for AI
Di Lorenzo
Elements of Machine Learning (including SciKitLearn) Scardapane
Fundamentals of Reinforcement Learning Santucci
School/project monitoring and soft-skills Baldassarre/Caligiore



Once acquired a solid “universalistic” and “interdisciplinary” Know-how on the key tools, a number of Specialistic Courses aims to provide the targeted specialisation in the particular field and approach of the selected School Track, according to the students academic interests and professional needs.

Deep Neural Networks: theory and technologies (Tensor Flow) Totaro/Valigi
Machine-learning project management and deployment (including cloud resources for deployment and training) Valigi
Visual Perception and Spatial Computing Freda
Systems Neuroscience Ferraina
Computational Embodied Neuroscience Baldassarre/Cartoni/Caligiore
Studying brain diseases through computational models Caligiore/Silvetti
Social Cognition Andrighetto
Computational Social Science and Agent Based Models Paolucci/ Cecconi/Payette



A range of Focussed Courses completes the School curriculum, to widen the perspective on the related issues.

Deep Reinforcement Learning Di Palo
Bringing ML projects in businesses Mauro
Deep Learning for Sentiment Analysis Pugliese
AI Planning and Scheduling and some applications in Robotics Rasconi/Oddi
Brain mechanism of dependencies Puglisi/Cabib
Commercial and serious games Miglino/Schembri
Introduction to Physical Computing with Arduino Sperati
Towards Designing Emotional Socially Assistive Robots Özcan
Sociophysics Vilone
Advanced Netlogo Payette
Econophysics Cecconi
Evolutionary robotics Nolfi
Swarm Robotics Trianni
Model-based data analysis Pezzulo/Silvetti
Ethical issues and AI Baldassarre/Santucci/Caligiore
Writing scientific project proposals Baldassarre/Caligiore
Project management and Funding sources Fantini


The School provides also a number of activities designed to support the acquisition of cross-field knowledge and personal capabilities, such as Co-projects (with the possibility to actively cross participate in the project of another student, aiming to common publications, sharing of learned techniques, etc.); Plenary Student Meetings ( to present projects advancement and discuss specific issues related to the project); Broadening Seminars (monthly, in synergy with ISTC-CNR seminars and other involved universities and companies); Deepening Seminars (2 hours every two weeks (to actively study and discuss it in depth a specific scientific article or application technique) and Forum Activities (free participation to the forum-related activities organised around the School.