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|
|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|
|Making a startup, project management||Paradisi, Sebastien, Amici|
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|
|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|
|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|
|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.