IM931 Interdisciplinary Approaches to Machine Learning

Spring 2019
Centre for Interdisciplinary Methodologies
University of Warwick

This module serves as an interdisciplinary introduction to contemporary machine learning research and applications, specifically focusing on the techniques of deep learning which use convolutional and/or recurrent neural network structures to both recognize and generate content from image, text, signals, sound, speech, and other forms of predominantly unstructured data. Using a combination of theoretical/conceptual/historical analysis and practical projects in the programming languages R or Python, the module will teach both the basic application of these techniques while also conveying the historical origins and ethical implications of such applications.

  • Week 01. Introduction: A Social History of Machine Learning.
  • Week 02. Table to Symbol: Structured Data, Unsupervised Classification, and Organizations.
  • Week 03. Sequence to Symbol: Text, Entextualization, and Contextualization.
  • Week 04. Image to Symbol: Convolutional Neural Networks (CNNs), Supervised Classification, and Iconicity.
  • Week 05. Image to Image: CNNs (con’t.); DeepDream, Style Transfer, and Theories of Aesthetics.
  • Week 06. Generative Adversarial Networks, Creative AI, and the Habitus.
  • Week 07. Sequence to Sequence: Recurrent Neural Networks (RNNs), Machine Translation, Structuralism and Poetics.
  • Week 08. Signals: Speech, Sound, and Temporality.
  • Week 09. Agency: Reinforcement Learning, Autonomous Agents, and Theories of Action.

Illustrative Bibliography

  • Anderson, James A./Rosenfeld, Edward, editors: Talking Nets: An Oral History of Neural Networks. MIT Press, 1998.
  • Baltrušaitis, T., Ahuja, C., & Morency, L.-P. (2017). Multimodal Machine Learning: A Survey and Taxonomy. ArXiv:1705.09406 [Cs].
  • Belting, H. (2011). An Anthropology of Images: Picture, Medium, Body. (T. Dunlap, Trans.). Princeton: Princeton University Press.
  • Berger, John. 1972. Ways of Seeing. London: Penguin.
  • Bourdieu, P. 1977. Outline of a Theory of Practice. Cambridge: Cambridge University Press.
  • Breiman, L. (2001). Statistical Modeling: The Two Cultures.
  • Chollet, F. (2018). Deep Learning with R (1 edition). Shelter Island, NY: Manning Publications.
  • Dreyfus, Hubert L./Dreyfus, Stuart E.: Making a Mind versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint. Daedalus, 117 1988, Nr. 1, 15–43
  • Deacon, Terrence W.: The Symbolic Species: The Co-evolution of Language and the Brain. W. W. Norton and Company, 1997.
  • Domingos, P. (2017). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. London: Penguin.
  • Dupuy, Jean-Pierre: On the origins of cognitive science : the mechanization of the mind. Princeton University Press, 2000
  • Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative Adversarial Networks, Generating “Art” by Learning About Styles and Deviating from Style Norms. ArXiv:1706.07068 [Cs].
  • Espeland, Wendy Nelson/Stevens, Mitchell L.: Commensuration as a Social Process. Annual Review of Sociology, 24 1998, Nr. 1, 313–343
  • Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. 2015. “A Neural Algorithm of Artistic Style.” arXiv:1508.06576 [cs, Q-Bio], August.
  • Hayles, N. Katherine: How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. University of Chicago Press, 1999
  • Haugeland, J.: Artificial Intelligence: The Very Idea. Cambridge, Mass.: MIT Press, January 1989.
  • Jakobson, Roman. 1971. “On Linguistic Aspects of Translation.” In Selected Writings II: Word and Language, 260–66. The Hague: Mouton.
  • Kockelman, P. (2013). The anthropology of an equation. Sieves, spam filters, agentive algorithms, and ontologies of transformation. HAU: Journal of Ethnographic Theory, 3(3), 33–61.
  • Krizhevsky, Alex/Sutskever, Ilya/Hinton, Geoffrey E.: ImageNet Classification with Deep Convolutional Neural Networks. 26th Annual Conference on Neural Information Processing Systems 2012.
  • Langley, Pat: The changing science of machine learning. Machine Learning 2011.
  • LeCun, Y. et al.: Backpropagation Applied to Handwritten Zip Code Recognition. Neural Comput. 1 December 1989, Nr. 4, 541–551.
  • Lévi-Strauss, Claude: Structural Anthropology. New Ed edition edition. New York: Basic Books, May 1974, ISBN 978–0–465–09516–2.
  • Lizardo, Omar: The Cognitive Origins of Bourdieu’s Habitus. Journal for the Theory of Social Behaviour, 34 December 2004, Nr. 4, 375–401.
  • Mackenzie, Adrian: The production of prediction: What does machine learning want? European Journal of Cultural Studies, 18 2015, Nr. 4-5, 429–445.
  • Mackenzie, A. (2017). Machine Learners: Archaeology of a Data Practice. Cambridge, MA: MIT Press.
  • Manning, Christopher D.: Computational Linguistics and Deep Learning. Computational Linguistics, 41 2015, Nr. 4, 701–707. Rumelhart, David E./Hinton, Geoffrey E./Williams, Ronald J.: Learning representations by back- propagating errors. Nature, 323 October 1986, Nr. 6088, 533–536
  • Saussure, F. de. (1915). Course in General Linguistics. McGraw-Hill.
  • Selfridge, O. G.: Pattern Recognition and Modern Computers. In Proceedings of the March 1-3, 1955, Western Joint Computer Conference. New York, NY, USA: ACM, 1955, AFIPS ’55 (Western).
  • Silverstein, M. (2003). Translation, Transduction, Transformation: Skating “Glossando” on Thin Semiotic Ice. In P. Rubel & A. Rosman (Eds.), Translating Cultures: Perspectives on Translation and Anthropology (pp. 75–105). Oxford.
  • Stone, M: Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society. Series B (Methodological), 36 1974, Nr. 2, 111–147.
  • Suchman, Lucy A.: Plans and situated actions : the problem of human-machine communication. Cambridge University Press, 1987.
  • Sutskever, Ilya/Vinyals, Oriol/Le, Quoc V.: Sequence to Sequence Learning with Neural Networks. arXiv:1409.3215 [cs], September 2014.
  • Underwood, T. (2015). The literary uses of high-dimensional space. Big Data & Society, 2(2).
  • Zeiler, Matthew D., and Rob Fergus. 2014. “Visualizing and Understanding Convolutional Networks.” In In Computer Vision–ECCV 2014, 818–33. Springer.