Teaching Machine Learning in the Digital Humanities at DH2024

In recent years, advancements in machine learning (ML) have opened exciting new capabilities for the computational humanities, giving rise to new pedagogical initiatives to teach applied ML specifically in a DH context.

This workshop will meet at DH2024: Reinvention and Responsibility to share experiences, best practices, and strategies for teaching ML—its techniques, potentials, and risks—to the humanities community.

The presenters and participants of this workshop teach (or plan to teach) ML to humanists at multiple levels—undergraduates, graduate students, as well as faculty and advanced researchers—and in various modalities—in-person, hybrid, and asynchronous.

Attendance is open to any technologist, researcher, librarian or student who is either interested in refining their methodology of teaching ML in the humanities context, or curious to embark on teaching ML to humanists.

All registered participants at DH 2024 may sign up to participate in this workshop. See the conference website for more information.

Instructors/organizers: Melanie Walsh (University of Washington), Quinn Dombrowski (Stanford University), Zoe LeBlanc (University of Illinois, Urbana-Champaign), Andrew Janco (University of Pennsylvania), Toma Tasovac (DARIAH-EU), Natalia Ermolaev (Princeton University), Nick Budak (Stanford University)