Lab: generative artificial intelligence literacy
A.A. 2025/2026
Obiettivi formativi
Non definiti
Risultati apprendimento attesi
Non definiti
Periodo: Terzo trimestre
Modalità di valutazione: Giudizio di approvazione
Giudizio di valutazione: superato/non superato
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Terzo trimestre
Programma
The following is a TBC breakdown of the module, each unit consisting of 45/60 minutes of work.
Here 'l' stands for 'lecture' and 'p' stands for 'practicum' or 'lab.'
l.0
A brief introduction to AI
l.1
A stats refresher
p.0
Handling text with computers
l.2, l.3
A brief introduction to Natural Language Processing
p.1
Online LLM prompts
l.4
Attention
l.5
Diffusion
p.2
Enriching prompts
l.6
The text-to-image bridge
p.3
Embedding prompts
p.4
Evaluating responses
l.7
Data sources and ethical aspects
l.8
Comparing and evaluating generated text
p.5
Huggingface
p.6
Onboard GenAI solutions
p.7
GenAI on your laptop
l.9
Conclusions and exam project instructions
p.8, p.9
Work on personal project, with supervision.
Here 'l' stands for 'lecture' and 'p' stands for 'practicum' or 'lab.'
l.0
A brief introduction to AI
l.1
A stats refresher
p.0
Handling text with computers
l.2, l.3
A brief introduction to Natural Language Processing
p.1
Online LLM prompts
l.4
Attention
l.5
Diffusion
p.2
Enriching prompts
l.6
The text-to-image bridge
p.3
Embedding prompts
p.4
Evaluating responses
l.7
Data sources and ethical aspects
l.8
Comparing and evaluating generated text
p.5
Huggingface
p.6
Onboard GenAI solutions
p.7
GenAI on your laptop
l.9
Conclusions and exam project instructions
p.8, p.9
Work on personal project, with supervision.
Prerequisiti
Basic computer literacy as taught in most BA/BSc courses.
Familiarity with any Linux/MacOs/Win filesystem and with Spreadsheet operations is preferable.
The lab part might involve running simple Python scripts.
Familiarity with any Linux/MacOs/Win filesystem and with Spreadsheet operations is preferable.
The lab part might involve running simple Python scripts.
Metodi didattici
About half of the class time will be allocated to frontal lectures explaining computer-based text handling and generation.
The other half will be hands-on accessing and experimenting Generative AI solutions.
We will move from online queries to HuggingFace to running private instances on one's laptop.
At the time of writing the choice engine for the latter is NVIDIA Memotron Nano.
The other half will be hands-on accessing and experimenting Generative AI solutions.
We will move from online queries to HuggingFace to running private instances on one's laptop.
At the time of writing the choice engine for the latter is NVIDIA Memotron Nano.
Materiale di riferimento
Contents, resources and study materials will be made available weekly from the class repository and linked from the relevant MyAriel page.
Materials seen in class are kept in a Github repository maintained by the instructor:
https://github.com/ale66/learn-genai
Background readings, in-class presentations, their order and the study materials are constantly reviewed, updated and amended to adjust to the pace of the class.
Materials seen in class are kept in a Github repository maintained by the instructor:
https://github.com/ale66/learn-genai
Background readings, in-class presentations, their order and the study materials are constantly reviewed, updated and amended to adjust to the pace of the class.
Modalità di verifica dell’apprendimento e criteri di valutazione
A take-home personal project that is related to the student's upcoming graduation project.
Docente/i
Ricevimento:
Il ricevimento viene svolto in forma telematica, previo appuntamento da concordare via mail
Piattaforma MS Teams