All Courses
AS-AI aims to train the next generation of interdisciplinary researchers, professionals and leaders to address their challenging problems by applying 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 choose to attend all modules (Full School), alternatively, the student can select among different modules 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. You could compute the cost of you personalised learning path through the Pre-enrollment form here.
At the end of the AS-AI, it is possible to be selected for an advanced project, which may lead to a publication and open the way to a possible PhD.
TITLE MODULES | HOURS | TITLE COURSES | HOURS | TEACHER | PROPAEDEUTICITIES |
FULL SCHOOL | 262 | ALL COURSES | 260 | ALL | – |
INTRODUCTION AND SEMINARS | 16 | Introduction to the School and AI | 4 | Baldassarre – Caligiore | No propaedeuticity |
Job in the digital era | 4 | Morabito | |||
Prosthetics and biomedical robotics | 4 | Zollo | |||
Robotics for Elder Care | 2 | Cortellessa – Fracasso | |||
Collective Intelligence for Decision Support: Theory, Practice and Applications in Medical Diagnostics | 2 | Trianni | |||
PYTHON PROGRAMMING AND DEPLOYMENT | 38 | Python, Basic programming tools (Anaconda, Colab) | 28 | Tamantini | No propaedeuticity |
Application Versioning (github) and Deployment (docker, web app) | 10 | Moscatelli | |||
MATHS AND PROGRAMMING | 30 | Maths for AI | 12 | Baldassarre | No propaedeuticity |
Linear Algebra for AI with Python | 10 | Caligiore | |||
Basic statistic for AI | 4 | Capirchio | |||
Probability theory for AI | 4 | Cartoni | |||
MACHINE LEARNING BASICS | 58 | Big data analysis with Pandas | 8 | Cecconi | MATH AND PROGRAMMING, PYTHON PROGRAMMING AND DEPLOYMENT |
Numerical optimisation | 8 | Carli | |||
Machine Learning | 28 | Merone | |||
Elements of deep learning | 14 | Capirchio | |||
MACHINE LEARNING ADVANCED | 70 | Deep Learning for computer vision | 12 | Capirchio | MATH AND PROGRAMMING, PYTHON PROGRAMMING AND DEPLOYMENT,MACHINE LEARNING BASICS |
AI Lab: ML vs deep network for motion perception |
4 | Tamantini | |||
Natural Language Processing | 12 | Reforgiato | |||
AI Lab: Deep networks for NLP |
8 | De Persiis | |||
Time series analysis, Transformers | 16 | Bacco-D’Antoni | |||
Data augmentation | 4 | D’Amore | |||
Large Language Models: prompting, RAG | 8 | Basile | |||
Development and Deployment of LLM Web App | 6 | Gnocchi | |||
BRAIN MODELLING | 34 | AI for system neuroscience | 4 | Baldassarre-Caligiore | MATH AND PROGRAMMING,PYTHON PROGRAMMING AND DEPLOYMENT |
Probabilistic computational models of brain | 12 | Cartoni | |||
Firing-rate computational models of brain | 12 | Baldassarre | |||
Modelling brain disorders through differential equations |
6 | Caligiore | |||
ETHICAL AND LEGAL ASPECTS OF AI | 12 | AI impact: individual, social, and technological anchors when everything changes | 8 | Baldassarre | No propaedeuticity |
EU and national AI legislation | 4 | Fasano | |||
Student assessment | 4 | AI Lab: Final students test | 4 | Caligiore | MATH AND PROGRAMMING,PYTHON PROGRAMMING AND DEPLOYMENT, MACHINE LEARNING BASICS |