Models and Machines 🤖 Revised Patterns and Outliers ⚖️

Class Activities

• Review Introduction to Pandas and Notebooks, along with APIS
• Erin Davis “What physical traits are most tied to gender in literature? Eye roll: Women are all soft thighs and red lips.” The Pudding July 2020 https://pudding.cool/2020/07/gendered-descriptions/ and https://pudding.cool/process/pitching-gendered-descriptions/

• Ted Underwood. “Seven ways humanists are using computers to understand text”. June 4, 2015 https://tedunderwood.com/2015/06/04/seven-ways-humanists-are-using-computers-to-understand-text/

• Ted Underwood “Why and Age of Machine Learning Needs the Humanities” Public Books https://www.publicbooks.org/why-an-age-of-machine-learning-needs-the-humanities/
• Maciej Cegłowski. “Deep Fried Data” Idle Words. September 27, 2016. https://idlewords.com/talks/deep_fried_data.htm
• Laura B. McGrath “Charisma (Embodiment): a Response to Tess McNulty” https://post45.org/2019/05/charisma-embodiment-a-response-to-tess-mcnulty/
• Explore AI Dungeon https://play.aidungeon.io/
• Review Introduction to Text Mining and and start NER ALL moved to following weeks

Additional Materials

• Matt Daniels “Hip Hop Words” https://pudding.cool/2017/09/hip-hop-words/
• Ted Underwood, David Bamman, and Sabrina Lee “The Transformation of Gender in English-Language Fiction,” Journal of Cultural Analytics, 2018, https://doi.org/10.22148/16.019
• For those interested in topic modeling, you may want to read Benjamin Schmidt (2013). “Words Alone: Dismantling Topic Models in the Humanities”. Journal of Digital Humanities.http://journalofdigitalhumanities.org/2-1/words-alone-by-benjamin-m-schmidt/