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.

Python for AI
Basic programming tools (Anaconda, Colab) 2 Capirchio Python programming, introduction to using software tools to write and share AI projects No propaedeuticity
Python 28 Mattioli
Versioning (github, docker) 6 Mattioli
Maths for AI
Maths for AI 10 Baldassarre Essential mathematics for AI (algebra, functions, limits, derivatives, linear algebra, probability theory) No propaedeuticity
Linear Algebra for AI with Python 10 Caligiore
Probability theory for AI 4 Cartoni
Machine Learning I
Numerical optimisation 8 Battiloro Development phases of algebraic-statistical algorithms for machine learning. Practical examples
Maths for AI, Python for AI, Big data Analysis
Machine Learning Applications

Computational Embodied Neuroscience

Computational Social Science
Machine Learning 28 Merone
Machine Learning II
Data augmentation 4 D’Amore
Time series analysis, Transformers, Techniques for data augmentation
Maths for AI, Python for AI, Big data Analysis, Machine Learning I, Deep Learning, NLP
Machine Learning Applications

Computational Embodied Neuroscience

Computational Social Science
Time series analysis, Transformers 16 Bacco-D’Antoni
Machine Learning Applications
Deployment of AI 6
Maths for AI, Python for AI, Big data Analysis, Machine Learning I, Deep Learning, NLP
Large Language Models: prompting, RAG 6
Big data Analysis
Big data analysis with Pandas 8 Cecconi Preparing data in order to develop an AI system. Practical examples
Maths for AI, Python for AI, Machine Learning
Data pre-processing for ML 4 Mirino
Deep Learning
Deep Learning: theory and practice 18 Capirchio Simple and deep artificial neural networks. Practical examples of the development of the main neural architectures for deep learning
Python for AI, Maths for AI, Big data Analysis
AI Lab: ML vs deep network for motion perception 4 Tamantini
AI Lab: Deep networks with Keras and application for vision 8 Capirchio
Natural Language Processing 12 Reforgiato Linguistic data transformation techniques. Development of ML, deep learning and symbolic algorithms for NLP and sentiment analysis
Python for AI, Maths for AI, Big data Analysis, Machine Learning, Deep learning
Machine Learning Applications

Computational Social Science
AI Lab: Deep networks for NLP 8 DePersis
Knowledge graph and semantic web 2 Nuzzolese
System Neuroscience
AI for system neuroscience 6 Baldassarre-Caligiore AI techniques for studying the brain using a systems approach No propaedeuticity
Computational Embodied Neuroscience
Fundamentals of neurobiology 4 Silvetti
Brain mechanisms of dependencies 2 Puglisi
Probabilistic computational models of brain 12 Cartoni Computational models at different levels of abstraction (single neuron, spinking neuron networks, leaky-integrators, connectionist, probabilistic, differential equation) to study the brain and neurodegenerative diseases
Python for AI, Maths for AI, System neuroscience
Computational Embodied Neuroscience
Firing-rate computational models of brain 12 Baldassarre
Modelling brain disorders through differential equations 6 Caligiore
AI Lab: Spiking neural networks 4 Mirino
AI Lab: Single neuron models 4 Torsello
AI for data-driven
brain modelling
8 Model-based data analysis 8 Pezzulo-Silvetti AI models for analysing and reproducing neurophysiological and brain imaging data
Machine Learning, Python for AI, Maths for AI
Computational Embodied Neuroscience
Autonomous robotics
and Reinforcement Learning

Open-ended robotics 12 Cartoni-Baldassarre AI for autonomous robotics and applications in medicine and rehabilitation
Maths for AI, Python for AI
Machine Learning Applications
Prosthetics and biomedical robotics 4 Zollo
Robotics for elder care 2 Cortellessa
Reinforcement Learning 8 Santucci
Social simulations
Social simulations 6 Marconi Development of AI models for the study of social phenomena. AI-related legal aspects
Python for AI, Math for AI
Computational Social Science
Socio/economic modelling by Python 6 Cecconi
EU and national AI legislation 4 Fasano
Blockchain 4 Giuliano
AI impact 8 AI impact: individual, social, and technological anchors when everything changes 8 Baldassarre Impact of the AI revolution from an ethical and social perspective No propaedeuticity
AI Seminars
16 Projects management and design thinking 4 Afferni In-depth seminars on various topics related to the change brought about by the spread of Artificial Intelligence in the workplace and in the world of scientific research No propaedeuticity
AI Patenting & Tech Transfer 2 Berti
AI and autonomy 2 Falcone
Writing impactful scientific papers: tricks and key rules 2 Caligiore
Job in the digital era 4 Morabito
Student assessment
12 Basic project 4 Caligiore
Students will independently carry out a machine learning and deep learning project (Basic Project), implementing all the development phases, from the preparation of the data to the implementation and training of the algorithms, through to optimisation and performance comparison between different algorithms. The students will also take a final assessment test that will cover the main topics covered during the course
Maths for AI, Python for AI, Big data Analysis, Machine Learning, Deep learning
School projects contamination 4 Caligiore-Baldassarre
AI Lab: Final students test 4 Caligiore-Capirchio
Introduction to AI 4 Introduction to the School and AI 4 Baldassarre-Caligiore Introduction to the school’s courses and different approaches to artificial intelligence No propaedeuticity