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 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 choose to attend all courses (Full School), benefiting from a discount on the enrollment fee. Indeed, the cost of the single hour decreases linearly in the range [20, 10] Euro. 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 | DESCRIPTION | PROPAEDEUTICITIES | TRACKS |
| FULL SCHOOL | 334 | ALL COURSES | 334 | ALL | – | – | |
|
Python for AI
|
36
|
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
|
24
|
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
|
36
|
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
|
20
|
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 | |||||
|
Big data Analysis
|
12
|
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
|
30
|
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 | |||||
|
NLP
|
22
|
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
|
12
|
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 | |||||
|
Computational
neuroscience |
38
|
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 |
22
|
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 |
| AI for brain imaging and EEG | 14 | Porcaro-Mattioli | |||||
|
Autonomous robotics
and Reinforcement Learning |
26 | 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
|
20
|
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 | |||||
| Artificial Intelligence and VideoGames |
8 | Artificial Intelligence and VideoGames | 8 | Schembri | AI for ‘seriuos game’ development | Python for AI, Math for AI |
Machine Learning Applications Computational Social Science |
| 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 |