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
Basic programming tools (Anaconda, github, Colab)
School projects contamination Baldassarre/Caligiore
Python for AI  Mannella
Math for AI with Python Baldassarre/Caligiore
Numerical optimization Di Lorenzo
Probability theory for AI Baldassarre
Machine Learning Merone
AI Lab: First student test Merone
AI Lab: Second student test Caligiore/Capirchio
Social Events 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 Learning Program, according to the students academic interests and professional needs.

Deep network and tensor flow Mannella
AI Lab: ML vs deep network for motion perception Tamantini
Visual perception and spatial computing Freda
AI Lab: Deep Network for Vision Capirchio
Big data analysis with Pandas Cecconi
Fundamentals of reinforcement learning Santucci
Natural Languages Processing Reforgiato
AI Lab: Deep Network for NLP  De Persis
AI for Sentiment Analysis Pugliese
AI Lab: Deep Network for Sentiment Analysis Pugliese
Knowledge graph and semantic web Asprino/Nuzzolese
AI for telemedicine Cortellessa
Machine-learning project management and deployment Mauro
Probabilistic computational models of brain Cartoni
Firing-rate computational models of brain Baldassarre
Model-based data analysis Pezzullo/Silvetti
AI for Systems Neuroscience Baldassarre/Caligiore
AI for Modelling Brain Disorders Caligiore/Mirino
AI for brain imaging and EEG Mattioli/Mirino/Porcaro
AI Lab: building brain models Caligiore/Capirchio/Mirino
Net logo for social simulations Cecconi
Biomedical and prosthetic robotics Zollo
Brain Mechanism of Dependencies Puglisi/Cabib
Commercial and Serious Game Schembri


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

Facing the impact of AI (soft skill and ethical issues) Baldassarre
Writing scientific papers Caligiore
European and National Legislation on AI Fasano
Technological transfer: Making a start-up Fantini
Project Management Afferni
Blockchain Giuliano
 AI ethical aspects Tummolini

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.