AI for Archaeologists, with Python

About

Archaeology deals with the study of the human past, conducted through material remains, i.e. artefacts that were manufactured, used, and discarded in ancient times. One of the most important tasks is to classify the artefacts, determining chronology, cultural attribution, form, function and other features. Neural networks and deep learning are powerful tools for supporting and facilitating such tasks, often time-consuming and heavily depending on prior knowledge and expertise.

The “AI for Archaeologists, with Python” Winter School illustrates the use of neural networks for analysing and classifying multimodal data, such as images, tables, and texts. It is conducted, with a hands-on approach, through Python, one of the main programming languages of AI and Data Science, including a wide variety of deep learning tools and network architectures. In order to effectively conduct and support the analysis and classification of data coming from tables, images and texts, modern archaeologists should be able to deal with concepts and tools related to new technologies. Such skills are not present in a standard archaeology background, though they are fundamental even to effectively interact with ICT experts.

Bibliography: Stevens E., Antoga L., Viehmann T., Deep Learning with Pytorch, Manning (2020).

The Winter School will be activated with at least 10 students. The maximum number of participants is set to 40 students.

The Winter School will last 60 hours, from 2 to 13 February 2026, and it will take place on campus in Pisa, at the Department of Civilisations and Forms of Knowledge, in Via Trieste, 40.

The program will be activated also in distance learning mode (TEAMS platform)

Course leader

Prof. Gabriele Gattiglia

Target group

Students, graduates, PhD candidates, and post-docs in archaeology or related to Cultural Heritage

Course aim

The Winter School will enable participants to explore and visualize data with Python, set up and train neural networks from scratch and/or by modifying pre-trained networks (transfer learning), in order to perform classification tasks based on images and/or texts. It is built around a new paradigm, which takes into consideration archaeologists as both producers and users of digital archaeological data.
A key component of the Winter School will focus on using effective prompting strategies encouragind a critical and informed use of Large Language Models, such as ChatGPT, Gemini or Copilot

Fee info