Digital logic design: from combinational and arithmetic circuits to sequential logic and hardware description languages.
Il MOOC propone una riflessione pedagogica sulle competenze necessarie alle persone per vivere, imparare, lavorare in contesti mediati dall’Intelligenza Artificiale (IA) generativa, mettendo in evidenza la centralità delle virtù, in particolare della saggezza pratica (phronesis).
The MOOC offers a pedagogical reflection on the competences people need to live, learn and work in environments mediated by generative Artificial Intelligence (AI), highlighting the centrality of the virtues, in particular practical wisdom (phronesis).
Learn to program in Python from scratch: syntax, control flow, functions, data structures, files and errors, up to small programs that are well organized, tested and readable.
Understand the design and synthesis of digital logic, with a specific focus on sequential logic and hardware description languages.
De la théorie à la pratique pour garantir des résultats de mesure fiables et des décisions éclairées
Analyzing complex networks and developing machine learning and graph neural network models with Python and NetworkX, from static graphs to temporal networks and responsible AI.
Reading, cleaning, visualizing, and interpreting data with Python: a practical journey from raw tables to informed decisions, with no prior programming or statistics knowledge required.
Criteria and good practices for integrating artificial intelligence into university communication: clarity, consistency, inclusion and institutional responsibility.
Dai grafi complessi alle Graph Neural Networks: Machine Learning per reti reali con Python
Leggere, capire e usare i dati nel mondo reale
Come le macchine imparano dai dati
From theory to practice to ensure reliable measurement results and informed decision-making
Criteri e buone pratiche per integrare l’intelligenza artificiale nella comunicazione universitaria: chiarezza, coerenza, inclusione e responsabilità istituzionale.