- Docente responsabile
- PIETRO PINOLI
- CCS proponenti
- Ingegneria Biomedica
- CFU
- 2
- Ore in presenza
- 22
- N° max studenti
- 60
- Parole chiave:
- Artificial Intelligence, Federated Learning, PYTHON
- Tag
- Computer science, Artificial intelligence, Information technologies
Descrizione dell'iniziativa
Objectives
The initiative proposes an introductory and progressive pathway to Python programming, designed for students who wish to acquire useful and immediately applicable skills, even without specific prerequisites. The course guides participants from setting up the working environment and learning the fundamentals of the language to data analysis, initial machine learning models, and the basics of neural networks.
Particular attention will be devoted to understanding the role that these tools play today in engineering practice and research, with examples and applications of interest also in the biomedical field. In the final part of the program, the topic of Federated Learning will be introduced, presented as a modern paradigm for distributed learning in contexts where privacy, security, and the management of sensitive data are of central importance. The objective is not only to provide basic technical skills, but also to offer an accessible and informed understanding of the value of these emerging approaches.
Teaching tools
The activity will alternate short lectures, guided exercises, and hands-on sessions. Commonly used tools in academic and professional settings will be employed, including the terminal, Visual Studio Code, Python virtual environments, and libraries for scientific programming, data analysis, and machine learning.
The program will be structured progressively, through applied problems, incremental exercises, and practice-oriented coding activities, with particular attention to clarity of presentation and accessibility of the content. The goal is to offer a serious and rigorous learning experience that is at the same time engaging, concrete, and capable of showing how Python can become a creative tool for design, analysis, and development. Classroom discussion, the ability to reason about real-world cases, and, where possible, brief external contributions or case studies will also be encouraged, in order to connect the course content with research and innovation contexts.
Assessment criteria
Successful completion of the activity requires attendance of at least two-thirds of the lessons and the presentation of a final project.
The final project may be developed on a topic of choice, individually or in groups, and will aim to demonstrate the ability to use Python to conceive, structure, and communicate a system, an application, or a solution to a problem chosen by the participants. The project may consist of the development of a prototype, the implementation of a proof of concept, or a structured presentation of a project idea, provided that a clear understanding of the tools introduced during the course and their possible application in a real-world context emerges.
The final presentation will therefore represent a concluding moment of synthesis and enhancement of the learning path, aimed not only at assessing knowledge acquisition but also at evaluating the ability to apply the covered content in a personal and informed way.
Periodo di svolgimento
dal April 2026 a June 2026
Calendario
Mercoledì 22/04 – Ambiente di lavoro, Jupyter Notebook, ambienti virtuali e introduzione a Python – 17:30 - 19:30
Giovedì 23/04 – Python di base: tipi di dato, strutture di controllo, liste e funzioni – 17:30 - 19:30
Mercoledì 29/04 – Programmazione a oggetti essenziale, file e gestione degli errori – 16:30 - 19:30
Mercoledì 06/05 – NumPy, Matplotlib e introduzione pratica a Pandas – 17:30 - 19:30
Mercoledì 13/05 – Fondamenti di Machine Learning e introduzione a PyTorch – 16:30 - 19:30
Mercoledì 20/05 – PyTorch: primo modello, training loop e avvio del progetto finale – 16:30 - 19:30
Mercoledì 27/05 – Neural Networks con PyTorch e classificazione di immagini – 16:30 - 19:30
Giovedì 28/05 – Introduzione al Secure Federated Learning con Flower – 17:30 - 19:30
Mercoledì 03/06 – Laboratorio finale e presentazione dei progetti – 17:30 - 19:30