Schedule and Topics

8/28 (Th) Prologue: Representing, Operationalizing, . . .

Primary readings the lecture draws on, optional but encouraged (see syllabus for additional readings):

• Quist, “Laurelled Lives” (2017)
• So, Redlining Culture (2020): chap. 3 (Recognition: Literary Distinction and Blackness)
• Bengio, Ducharme, and Vincent, “A Neural Probabilistic Language Model” (2000)

---

Optional: math self-assessment; cf. Kun, A Programmer’s Introduction to Mathematics

9/2 (Tu) Lecture: Death of Theory

• Eliot, “Tradition and Individual Talent—I” (1919)
• Murphy II. 32.1–4 (Representation learning)
• Mikolov et al., “Distributed Representations of Words and Phrases and their Compositionality” (2013)

9/4 (Th) Annotation

• Benj Pasek and Justin Paul, Dear Evan Hansen (2017)

---

Optional: Python self-assessment; cf. Walsh, Introduction to Cultural Analytics & Python

9/9 (Tu) Lecture: Death of Subjectivity

• Wimsatt and Beardsley, “The Intentional Fallacy” (1946), up to §II; Piper, So, and Bamman, “Narrative Theory for Computational Narrative Understanding” (2021)
• Murphy II. 20.1–20.4.1 (Generative models: an overview)
• Vafa, Naidu, and Blei, “Text-Based Ideal Points” (2020)

9/11 (Th) Quiz 1 (in class)

9/16 (Tu) Lecture: Death of the Author

• Jakobson, “Closing Statement: Linguistics and Poetics” (1960)
• Weatherby and Justie, “Indexical AI” (2022)
• Murphy I. 15.1–3 (Neural sequence modeling)

9/18 (Th) Annotation

• David Angell, Peter Casey, and David Lee, Frasier: I.i (The Good Son), I.xxiv (My Coffee with Niles), VI.xvii (The Dinner Party)

---

Optional: math and Python review

9/23 (Tu) Lecture: Death of the Man

• Barthes, S / Z (1974), up to §XV
• Hayles, “Information or Noise? Economy of Explanation in Barthes’s S/Z and Shannon’s Information Theory” (1987)
• Tay, Luu, and Hui, “Compare, Compress and Propagate” (2018)

9/25 (Th) Annotation

• Oscar Wilde, The Importance of Being Earnest (1895)

---

9/26 (Fri) HW 1 due (close reading)

---

Optional: math and Python review

9/30 (Tu) Lecture: Imagetext

• Mitchell, Picture Theory (1995): chap. 1 (The pictorial turn)
• Vilnis and McCallum, “Word Representations via Gaussian Embedding” (2015)
• Ruhe et al., “Rolling Diffusion Models” (2024)

10/2 (Th) Quiz 2 (in class)

---

10/3 (Fri) Project proposal due (MAX 1 p.)

10/7 (Tu) Lecture: Time-Image

• Deleuze, Cinema 2 (1989): chap. 2 (Recapitulation of images and signs)
• Korbar, Huh, and Zisserman, “Look, Listen and Recognise” (2024)
• Sun et al., “video-SALMONN: Speech-enhanced Audio-Visual Large Language Models” (2024)

10/9 (Th) Workshop

Embeddings for prediction and clustering:

• Zhang et al., Dive Into Deep Learning: chs. 13–16 (skim)
• Murphy I. 21.3 (K-means clustering); 21.4 (Mixture models)
• Murphy II. 3.10 (Hypothesis testing)

10/14 (Tu) Lecture: Assemblage

• Deleuze and Guattari, A Thousand Plateaus (1987): Introduction; chap. 6
• Tsing, The Mushroom at the End of the World (2021): Introduction; chap. 4
• Fedus, Zoph, and Shazeer, “Switch Transformers” (2022)

10/16 (Th) Workshop

Inference and sampling:

• Wortsman et al., “Model Soups” (2022)
• Bertsch et al., “It’s MBR All the Way Down” (2023)
• Qineng Wang et al., “Rethinking the Bounds of LLM Reasoning” (2024)

10/21 (Tu) Lecture: Relationality

• Simondon, Individuation in Light of Notions of Form and Information (1964 [2021]): “Form and Matter”
• Ferreira Da Silva, Unpayable Debt (2019): chap. 2 (the most perfect hallucination)
• Bordes et al., “Translating Embeddings for Modeling Multi-relational Data” (2013)

10/23 (Th) Workshop

Wrap-up + writing and presentation:

• Sims, Park, and Bamman, “Literary Event Detection” (2019)
• Bode, “Why You Can’t Model Away Bias” (2020)
• Shechtman, “Command of Media’s Metaphors” (2021)

10/28 (Tu) Lecture: Form and Scale

• English and Underwood, “Shifting Scales” (2016)
• Klein, “Dimensions of Scale” (2020)
• Brown et al., “Language Models are Few-Shot Learners” (2020); skim: J. Kaplan et al., “Scaling Laws for Neural Language Models” (2020)

10/30 (Th) Seminar

• Lester, Al-Rfou, and Constant, “The Power of Scale for Parameter-Efficient Prompt Tuning” (2021)
• Levy, Jacoby, and Goldberg, “Same Task, More Tokens” (2024)
• Muennighoff et al., “s1: Simple Test-Time Scaling” (2025)

---

10/31 (Fri) HW 2 due (workshop notebook)

11/4 (Tu) Lecture: Form and Ideology

• Jameson, The Political Unconscious (1994): chap. 1 (On Interpretation)
• Clough, The User Unconscious (2018): “The Datalogical Turn”
• Ziems et al., “Can Large Language Models Transform Computational Social Science?” (2024)

11/6 (Th) Seminar

• Gururangan et al., “Don’t Stop Pretraining” (2020)
• Arora and Goyal, “A Theory for Emergence of Complex Skills in Language Models” (2023)
• Qunbo Wang et al., “Soft Knowledge Prompt” (2024)

---

9/19 (Fri) Project mid-term report due

11/11 (Tu) Lecture: Form and Interaction

• Hegel, Phenomenology of Spirit (1807 [2018]): section A (“Mastery and Servitude”)
• Goffman, Forms of Talk (1981): chap. 2 (“Replies and Responses”)
• Stiennon et al., “Learning to Summarize with Human Feedback” (2020)

11/13 (Th) Seminar

• Rafailov et al., “Direct Preference Optimization” (2023)
• Dingemanse and Enfield, “Interactive Repair and the Foundations of Language” (2024)
• Z. Liu et al., “A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration” (2024)

11/18 (Tu) Lecture: Form and Context

• Silverstein, Language in Culture (2022): Introduction; chap. 8 (“Knowledge”)
• Fish, “Is There a Text in This Class?” (1995)
• Jurgens et al., “Your Spouse Needs Professional Help” (2023)

11/20 (Th) Seminar

• Chevalier et al., “Adapting Language Models to Compress Contexts” (2023)
• Khanuja et al., “An Image Speaks a Thousand Words, but Can Everyone Listen?” (2024)
• Edge et al., “From Local to Global: A Graph RAG Approach to Query-Focused Summarization” (2024)

12/2 (Tu) Epilogue: What Is Cultural Analytics?

• Foucault, “What Is Enlightenment?” (1983)
• Mbembe, “The Universal Right to Breathe” (2021)

12/4 (Th) Project presentation (in class)

12/12 (Fri) Project final report due