Schedule

The following schedule runs from June 5 – 9, 2023. (Last January’s institute schedule is available on the Winter page.) Each day will involve a mix of seminar-style discussion and hands-on technical work. Morning sessions will be 10:00a to 12:30p, afternoon sessions will be 1:30p to 4:00p.

An orientation and tech setup day will be held on May 24, 2023, 2-4PM.

Monday: Introductions & Setting the Stage

How are data science methods being used in the humanities? How are humanists studying the automation of decision-making and cultural production?

Required Readings

*Must be logged into a Princeton institutional Google account to access these PDFs.

Brian Beaton et al., “Debating Data Science: A Roundtable,” Radical History Review, no. 127 (January 2017): 133–48.

Wai Chee Dimock, “AI and the Humanities,” PMLA 135, no. 3 (May 2020): 449–54.

Anne Helmreich, Matthew Lincoln, and Charles van den Heuvel, “Data Ecosystems and Futures of Art History,” Histoire de l’art, no. 87 (June 29, 2021): 45–54.

Barbara McGillivray et al., “The Challenges and Prospects of the Intersection of Humanities and Data Science: A White Paper from The Alan Turing Institute,” 2020.

Morning

10:00 – 10:30: Sierra Eckert and Ryan Heuser, Center for Digital Humanities; Discussion on the history of data science and recent trends in computational methods in the humanities.; Preview of what will be covered in each day of the Institute; Opening Slides
10:30 – 11:30: Participant introductions and project descriptions. What do you hope to learn at this Institute?
11:30 – 12:30: Discussion of readings.; Participants: come prepared to discuss how data science is intersecting with scholarship in your discipline.

Afternoon

1:30 – 2:00: Peter Ramadge, Gordon Y.S. Wu Professor of Engineering and Director of the Center for Statistics and Machine Learning.; Presentation on the data science ecosystem at Princeton.
2:00 – 2:30: Helmut Reimitz, Professor of History; Director, Program in Medieval Studies; Tim Geelhaar, SFB 1288 Praktiken des Vergleichens – Dateninfrastruktur and Digital Humanities, University of Bielefeld; Jan Odstrčilík, Institut für Mittelalterforschung, Austrian Academy of Sciences; Presentation on History Books and the History of the Book in the Middle Ages: applying computational tools to learn about the “history of histories” in medieval Europe.
2:30 – 3:00: Informal discussion.

Tuesday: Fundamentals

An introduction to the baseline statistical concepts used in machine learning, from distribution to regression. What does it mean to be “unsupervised”? How do we evaluate a model?

Readings

Meredith Broussard, “Machine Learning: The DL on ML,” in Artificial Unintelligence: How Computers Misunderstand the World (Cambridge, Massachusetts & London, England: The MIT Press, 2018).

Wendy Hui Kyong Chun, excerpt from Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition (Cambridge, Massachusetts & London, England: The MIT Press, 2021).

Richard So, “‘All Models Are Wrong,’” PMLA 132, no. 3 (May 2017): 668–73, https://doi.org/10.1632/pmla.2017.132.3.668.

Tony Chu and Stephanie Yee, “A Visual Introduction to Machine Learning,” R2D3.

Amy Winecoff, “Introduction to Machine Learning for the Humanities” (2023)

Morning

10:00 – 12:30

Sierra Eckert, Perkins Postdoctoral Fellow, Center for Digital Humanities

Humanities Data Fundamentals

Relevant files:

Humanities Data Fundamentals (slides)

Humanities-Data-Fundamentals.ipynb

Introduction to Text Analysis with Voyant

Afternoon

1:30 – 4:00

Amy Winecoff, DataX Fellow, Center for IT Policy & Center for Statistics and Machine Learning

Introduction to Machine Learning for the Humanities

Relevant files:

GitHub repository

Wednesday: History of Measurement

On the historical epistemology of quantification. How did measurement practices in the past cross the now-accepted divide between the sciences and humanities?

Morning

10:00 – 12:30: David Kinney, Postdoctoral Research Associate in Cognitive Science of Values, University Center for Human Values; Lecture on the history of measurement across the disciplines

Afternoon

1:30 – 4:00

David Kinney

Hands-On: Word Embeddings

Relevant files:

UPDATED - CDH_word_embedding_notebook.ipynb.ipynb

Readings

Hasok Chang, “Spirit, Air, and Quicksilver: The Search for the ‘Real’ Scale of Temperature,” Historical Studies in the Physical and Biological Sciences 31, no. 2 (2001): 249–84.

Thursday: Unpacking Our Code Libraries

On the use of industry-created tools for influential humanities scholarship. How did software for speech recognition and image tagging end up in a literary text analysis tool?

Readings

Selections from Richard So, Redlining Culture: A Data History of Racial Inequality and Postwar Fiction (Columbia University Press, 2020).

Ted Underwood, “Machine Learning and Human Perspective,” PMLA 135, no. 1 (January 2020): 92–109, https://doi.org/10.1632/pmla.2020.135.1.92.

Morning

10:00 – 12:30

Sierra Eckert, Perkins Postdoctoral Fellow, Center for Digital Humanities

Ryan Heuser, Research Software Engineer, Center for Digita Humanities

Useful Natural Language Processing

Relevant files:

HDSI-ThursSession.ipynb, part 1

Afternoon

1:30 – 4:00

Sierra Eckert, Ryan Heuser

Named Entity Recognition, Geocoding, and Mapping

Relevant files:

HDSI-ThursSession.ipynb, part 2

Friday: Participant Projects

An opportunity for participants to workshop their own project ideas. Moderated by Natalia Ermolaev and Meredith Martin.

Morning

10:00 – 11:00: Roundtable: Computational & Digital Resources on Campus; Natalia Ermolaev, moderator; Ian Cosden, Director, Research Software Engineering for Computational & Data Science [slides]; Jennifer Grayburn, Assistant Director of Digital and Open Scholarship [slides]; Ben Johnston, Senior Educational Technologist, McGraw Center for Teaching & Learning [slides]; Ariel Ackerly, Makerspace Specialist, Princeton University Library [slides]; Jonathan Halverson, Research Software and Computing Training Lead, Princeton Institute for Computational Science & Engineering
11:00 – 12:30: Participant Project Consultations; Grant Wythoff, moderator; How can participants apply what they have learned to their own discipline?; What research projects or introductory course ideas do participants want to explore?; CDH staff will help participants scope their idea to attainable goals while identifying what support or additional training they need.

Afternoon

1:30 – 3:00: Participant project consultations, continued.
3:00 – 4:00: Share feedback for next summer’s Institute in this exit survey.