Satisfies Elective A requirement
Application of unsupervised machine learning methods to the linguistic, cultural, and sociological analysis of long form textual sources. Students will engage with DH theory, history, and methods, as well as large digitized text libraries, structured data (relational databases, linked data, etc.), and programming language. Knowledge of Python and familiarity with probability theory required.
T/TH 12:30-1:45
Course
Working with materials, tools, and data from Collective Biographies of Women (CBW), a Scholars’ Lab and IATH database project, we will branch out from the Jubilee volume of 1897: Women Novelists of Queen Victoria’s Reign, in which living women novelists write chapter-length biographical-critical notices of deceased novelists, excluding the Regency and earlier figures. A prevailing question in the course will be the force of identity- and periodization-politics, so to speak: the metadata categories that classify women writers of fiction (and their literary settings) who hale from various...
Course
Tuesdays from 7:00pm - 9:30pm in Campbell Hall 158.
This is a course about information and data visualization. We live in a world rich with information. This course teaches visual and spatial thinking coupled with data analysis tools and custom web-enabled programming to construct and envision information. To find and even invent approaches toward seeing into complex problems, we will study, and make, useful, compelling and beautiful tools to see.
Course
Course
