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.

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. The student can select among different COURSES to build the personalized learning and training path based on her/his project, learning needs, and interests with the support of the School President, Director and of School Advisors. Alternatively, the student can select one of the three learning and training paths (Tracks) proposed by the School.


These courses, for the cross-cutting relevance of their subjects, 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.

Introduction to the School and AI Baldassarre, Caligiore
Soft skills Baldassarre/Sparro
School projects contamination Caligiore
Fundamentals of coding in Python Baldassarre
Python programming for AI (including Git, NumPy, Pandas, MatPlotLib) Mannella
Math analysis for AI Baldassarre/Caligiore
Elements of linear Algebra Afferni
Probability theory for AI Baldassarre
Patenting for technological transfer Garito
Technological transfer Fantini
Making a startup, project management Paradisi, Sebastien, Amici
Social Event ALL



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 Learning Program, according to the students academic interests and professional needs.

Numerical optimisation Di Lorenzo
Machine Learning and deep networks Merone
Deep network and tensor flow Mannella
Deep networks and vision: case study Capirchio
Big data/data analysis Cecconi
Fundamentals of reinforcement learning Santucci
Visual Perception and Spatial Computing Freda
Deep learning for sentiment analysis Pugliese
Machine-learning project management and deployment Mauro
Deep Reinforcement Learning Valigi
AI planning and scheduling for robotic applications Oddi/Rasconi/Cesta
Probabilistic computational models of brain Cartoni
Firing-rate computational models of brain Baldassarre
Spiking computational models of brain Caligiore/Mirino
Systems Neuroscience Ferraina
Model-based data analysis Pezzullo/Silvetti
Introduction to Physical Computing with Arduino Montedori/D’amore
Computational Social Science Andrighetto
Net logo for social simulations Cecconi
Legal issues and AI Lettieri
Artificial Intelligence and Video Games Schembri



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

Genetic algorithms for parameter optimization Mannella
Perception of motion: case study Tamantini
Natural Language Processing Reforgiato
Natural Language Processing: case study De Persis/D’Amore
Collective Robotics Nardi
Prostetica, robotica biomedica Zollo
Neuromorphic computing Del Giudice
Writing scientific papers Caligiore
Una nuova scienza dell’uomo Parisi
Brain mechanism of dependencies Puglisi/Cabib
Two days events: AI-ethics-law; AI-economics-jobs Baldassarre/Santucci
Legislazione Europea e Nazionale su AI Fasano
Block chain Giuliano/Vignali
Trust and Reputation Falcone
Challanges and Problems of Social AI Castelfranchi


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.