Dates: 19-23 June, 14:00 – 17:00
This five-day course will introduce graduate students to techniques for analyzing humanities data, with a focus on textual data. Topics will include feature extraction, exploratory analysis and data visualization, as well as a basic introduction to machine learning techniques such as topic modeling and support vector machine classifiers. No prior technical knowledge is needed for the course—while students will gain basic programming skills in R, the course’s emphasis will be on developing a conceptual understanding of techniques in order to think about how they might be used in future work. Along with the reading of core digital humanities texts, this technical understanding will be used to ground discussions of questions that are central to digital humanities work such as how aggregate, quantitative data can be used to understand situated cultural objects.
Daniel Carter is a PhD candidate in the School of Information at the University of Texas at Austin and will start as a professor of digital media at Texas State University in 2017. His research centers on how new analytic processes such as machine learning impact the knowledge production and work practices of various groups, including digital humanities scholars. He’s also interested in questions around infrastructure, labor and design. In addition to ethnographic methods, he often works with computational methods from natural language processing and social network analysis.
|Early bird offer (24th April – 19th May)||Standard Price (20th May – 9th June)|
A certificate of participation will be issued upon attendance of 75% of the course classes.
Maximum number of participants: 22
For doubts or questions related with registration, please email firstname.lastname@example.org or call 220 408 400.