Workshop: applications of computer vision in digital humanities
A.A. 2025/2026
Obiettivi formativi
This workshop introduces participants to the foundational concepts and methodologies of computer vision, with a particular focus on applications within the digital humanities. Through theoretical insights and practical tools, it aims to:
-Present key artificial intelligence techniques, especially supervised deep learning models.
-Explore computer vision tasks such as image classification, semantic segmentation, and text recognition.
-Demonstrate how to prepare and process visual datasets for humanities research.
-Foster an understanding of tools like Roboflow, Google Teachable Machine, and Transkribus for real-world applications.
-Present key artificial intelligence techniques, especially supervised deep learning models.
-Explore computer vision tasks such as image classification, semantic segmentation, and text recognition.
-Demonstrate how to prepare and process visual datasets for humanities research.
-Foster an understanding of tools like Roboflow, Google Teachable Machine, and Transkribus for real-world applications.
Risultati apprendimento attesi
By the end of this workshop, students will be able to:
-Understand Core Concepts: Explain the distinctions between AI, machine learning, and deep learning, and describe their relevance to computer vision.
-Apply CV Techniques: Identify and implement basic computer vision tasks, such as image classification, using appropriate tools.
-evelop Annotated Datasets: Create and manage labeled image datasets suitable for training classification models using platforms like Roboflow.
-Experiment with AI Tools: Use Google Teachable Machine for training simple classifiers and interpret the results.
-Process Historical Manuscripts: Apply convolutional neural networks for handwritten text recognition through tools such as Transkribus.
-Critically Engage with Humanities Data: Reflect on the methodological challenges and opportunities of applying CV in digital humanities contexts.
Students will also have access to all materials via the myAriel platform and can consult the instructor during office hours or by email.
-Understand Core Concepts: Explain the distinctions between AI, machine learning, and deep learning, and describe their relevance to computer vision.
-Apply CV Techniques: Identify and implement basic computer vision tasks, such as image classification, using appropriate tools.
-evelop Annotated Datasets: Create and manage labeled image datasets suitable for training classification models using platforms like Roboflow.
-Experiment with AI Tools: Use Google Teachable Machine for training simple classifiers and interpret the results.
-Process Historical Manuscripts: Apply convolutional neural networks for handwritten text recognition through tools such as Transkribus.
-Critically Engage with Humanities Data: Reflect on the methodological challenges and opportunities of applying CV in digital humanities contexts.
Students will also have access to all materials via the myAriel platform and can consult the instructor during office hours or by email.
Periodo: Secondo semestre
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
Secondo semestre
Programma
Questo laboratorio pratico di 20 ore offre un'introduzione critica e applicativa all'uso dell'Intelligenza Artificiale negli studi storici e culturali. Il corso adotta un approccio "no-code", focalizzandosi sulla costruzione della comprensione concettuale e delle competenze pratiche attraverso strumenti intuitivi e di facile utilizzo. Gli studenti passeranno dai concetti fondamentali dell'IA e della data literacy all'esperienza diretta con piattaforme user-friendly per la classificazione di immagini (Teachable Machine), il riconoscimento di oggetti (Roboflow) e il riconoscimento di testi manoscritti (Transkribus). Il corso è altamente interattivo e si conclude con un progetto finale guidato dagli studenti, in cui i partecipanti presenteranno un'analisi critica di un articolo accademico sull'IA nelle discipline umanistiche oppure i risultati di un piccolo progetto applicativo.
Prerequisiti
Non sono richieste conoscenze pregresse di programmazione o di informatica. È sufficiente una familiarità con l'uso del computer e delle applicazioni software di base. È invece fondamentale un forte interesse per l'esplorazione dell'intersezione tra tecnologie digitali e ricerca umanistica.
Metodi didattici
Il corso adotta un approccio di apprendimento attivo, combinando lezioni concettuali, discussioni critiche e laboratori pratici con strumenti digitali. La partecipazione attiva è essenziale per favorire un ambiente di apprendimento dinamico e collaborativo. La frequenza alle lezioni è obbligatoria.
Materiale di riferimento
1) Slides, handouts, and scholarly articles provided by the instructor
2) Books on computer vision to support learning (these are not a prerequisite, just references):
- Foundations of Computer Vision by Antonio Torralba, Phillip Isola, and William T. Freeman (MIT Press, 2024): A comprehensive introduction to core computer vision concepts, suitable for those new to the field. (Online https://mitpress.ublish.com/ebook/foundations-of-computer-vision-1/12791/418)
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Adaptive Computation and Machine Learning series): A more advanced text focused on deep learning techniques, ideal for students seeking in-depth understanding of the algorithms behind computer vision. (online: https://www.deeplearningbook.org/)
- Biological and Computer Vision by Gabriel Kreiman (Cambridge University Press, 2021): Explores both biological and computational vision, providing useful insights into vision systems and their application in computer vision, accessible for students with a basic understanding of the subject. (online: https://klab.tch.harvard.edu/publications/Books/BiologicalAndComputerVision/TableOfContents.html)
- Artificial Intelligence, A Guide for Thinking Humans by Melanie Mitchell (2019): Provides a clear and accessible overview of AI concepts, challenges, and the history of the field. It is a helpful resource for students seeking to understand the broader context of AI and its implications beyond computer vision (https://melaniemitchell.me/aibook/)
- Pixels & Paintings: Foundations of Computer-assisted Connoisseurship by David G. Stork (2023): A specialized resource connecting computer vision to art and heritage analysis, can be useful for students interested in art and cultural heritage applications.
3) Transkribus: Offers valuable online resources for handwritten text recognition. The platform provides guides and tools for working with historical manuscript images, suitable for students engaged in transcription tasks: https://www.transkribus.org/blog
2) Books on computer vision to support learning (these are not a prerequisite, just references):
- Foundations of Computer Vision by Antonio Torralba, Phillip Isola, and William T. Freeman (MIT Press, 2024): A comprehensive introduction to core computer vision concepts, suitable for those new to the field. (Online https://mitpress.ublish.com/ebook/foundations-of-computer-vision-1/12791/418)
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Adaptive Computation and Machine Learning series): A more advanced text focused on deep learning techniques, ideal for students seeking in-depth understanding of the algorithms behind computer vision. (online: https://www.deeplearningbook.org/)
- Biological and Computer Vision by Gabriel Kreiman (Cambridge University Press, 2021): Explores both biological and computational vision, providing useful insights into vision systems and their application in computer vision, accessible for students with a basic understanding of the subject. (online: https://klab.tch.harvard.edu/publications/Books/BiologicalAndComputerVision/TableOfContents.html)
- Artificial Intelligence, A Guide for Thinking Humans by Melanie Mitchell (2019): Provides a clear and accessible overview of AI concepts, challenges, and the history of the field. It is a helpful resource for students seeking to understand the broader context of AI and its implications beyond computer vision (https://melaniemitchell.me/aibook/)
- Pixels & Paintings: Foundations of Computer-assisted Connoisseurship by David G. Stork (2023): A specialized resource connecting computer vision to art and heritage analysis, can be useful for students interested in art and cultural heritage applications.
3) Transkribus: Offers valuable online resources for handwritten text recognition. The platform provides guides and tools for working with historical manuscript images, suitable for students engaged in transcription tasks: https://www.transkribus.org/blog
Modalità di verifica dell’apprendimento e criteri di valutazione
La valutazione complessiva delle competenze teoriche e applicative acquisite si baserà su un esame orale finale incentrato sui materiali didattici forniti (50%), sulla preparazione di lavori individuali o collettivi (30%) e sulla partecipazione attiva durante le lezioni (20%).
L'esame orale finale consiste in una discussione orale, volta a valutare la comprensione da parte degli studenti dei principali concetti introdotti durante il corso, la loro capacità di esporli con chiarezza e di collegare i diversi argomenti trattati, nonché la loro attitudine ad analizzare casi di studio con strumenti teorici adeguati e con consapevolezza critica.
L'esame orale finale consiste in una discussione orale, volta a valutare la comprensione da parte degli studenti dei principali concetti introdotti durante il corso, la loro capacità di esporli con chiarezza e di collegare i diversi argomenti trattati, nonché la loro attitudine ad analizzare casi di studio con strumenti teorici adeguati e con consapevolezza critica.
Docente/i
Ricevimento:
Ingresso B, 3° piano, studio 3014 (A16)