Academic year 2021 2022

During this academic year, I teach the following courses (in chronological order):

Libraries have always been an inspiration for the standards and technologies developed by semantic web activities. At the end of the course the students will learn how to manage the process related to a DL creation: from the metadata choice to the ontologies selection; from the network issues to the architecture implementation; from the preservation of data to the curation of the life cycle of digital cultural objects.

AI is rapidly becoming part of our daily lives in many visible and invisible ways. Consequently, the social sciences and humanities are increasingly contributing to dominant research problems in AI. Examples include the ethical profiling of AI systems, including their privacy and governance, questions of fairness and accountability, as well as data bias and model interpretability. This course provides an opportunity to engage with these increasingly mainstream research questions. It does so by proposing a pathway into the main critical themes emerging when AI is applied in real-world scenarios, and it does so by highlighting the bi-directional contributions of the social sciences and humanities.

In this course, you will learn how to code in Python and analyze data on socio-cultural phenomena. Socio-cultural data pose great challenges and offer many opportunities due to their variety and richness. Examples include historical and literary sources, social networks and media. The course offers a pathway into applied data analysis concepts and techniques, with applications in the arts and humanists.

During the second part of this course, students will exclusively focus on an individual or group project, possibly as part of a larger research project, giving them ample opportunity to explore a research question, dataset, and technique(s). Students will be particularly encouraged to work on a topic of their choice.