Covers foundations and applications of NLP with a focus on the most popular form of unstructured data - text. Convert source texts into structure-preserving analytical form and then apply information theory, NLP tools, and vector-based methods to explore language models, topic models, sentiment analyses, and GenAI techniques. Focus is on unsupervised methods to explore cognitive patterns in texts, with real-world examples and demonstrations.

In this course, students learn about the development of media technologies and infrastructures: how and why they were built, how they were shaped by regulation, and the social and political concerns driving both technological development and regulation. Students will read and assess primary and secondary literature, gaining an understanding of historiographical methods and employing those methods to produce original historical research.

This workshop introduces advanced humanities students to map history research and geospatial visualization. It features work with maps in Special Collections as well as the production of digital scholarship using ArcGIS software. No experience is expected or required. This course counts as an elective for the DH Graduate Certificate program. Prerequisite: Graduate student or College 3rd or 4th year.

This course starts from the premise that whatever it may mean technologically “artificial intelligence” is also a discursive formulation, which is to say an active signifier in the cultural imagination. To begin working towards some understandings of that signifier we will undertake readings of "AI" in multiple registers: current critical writing that allows us to consider not only how today’s generative AI technology works but what work—political, social, aesthetic—we are calling upon it to do; representations of AI in literary fiction, from the 18th century to the present; and writing actually written by AI in the form of creative and experimental texts whose provenance and agency is indeterminate. While the focus of the course will be critical and theoretical (with no technical background required or assumed) we will also engage in some hands-on experimentation of our own. And while pedagogy will not be our focus, the course will offer a conceptual grounding for those who plan to teach in a world where AI is prevalent. Requirements will include leading discussion, a short reflective paper, and a longer semester project which could be either critical or creative.