Lo studente ha la libertà di scegliere tra due percorsi: partecipare all’intero programma (Full School) per un’esperienza formativa completa, oppure selezionare specifici moduli per creare un percorso su misura, perfettamente adattato ai propri obiettivi, interessi e necessità di apprendimento.
Potete calcolare il costo del vostro percorso di apprendimento personalizzato attraverso il modulo di pre-iscrizione qui.
| MODULI | ORE | CORSI | ORE | INSEGNANTE | PROPEDEUTICITA’ |
| FULL SCHOOL | 262 | TUTTI I CORSI | 262 | Tutti | – |
| INTRODUCTION AND SEMINARS |
16 | Introduction to the School and AI | 4 | Baldassarre / Caligiore | No propedeuticità |
| Job in the digital era | 4 | Morabito | |||
| Prosthetics and biomedical robotics | 4 | Zollo | |||
| Robotics for Elder Care | 2 | 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 propedeuticità |
| Application Versioning (github) and Deployment (docker, web app) | 10 | Moscatelli | |||
| MATHS AND PROGRAMMING |
30 | Maths for AI | 12 | Baldassarre | No propedeuticità
|
| 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 |
Python programming and Deployment, Maths and programming
|
| 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 |
Python programming and Deployment, Maths and programming, 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 | DePersis | |||
| 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 |
Python programming and Deployment, Maths and programming
|
| 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 ASTS 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 | Final students test | 4 | Caligiore |
|