Interdisciplinary Approaches to Machine Learning
Cross-Disciplinary Postgraduate Modules
IM931 Interdisciplinary Approaches to Machine Learning
15/20/30 CATS (7.5/10/15 ECTS)
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 programming projects in the R programming language, 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.
- 40% Laboratory Assignment and Report (15 CATS: 2000 words, 20/30 CATS: 3000 words)
- 10% Group Presentation
- 50% Final Project and Report (15 CATS: 2000 words; 20 CATS: 3000 words; 30 CATS: 4000 words)
- 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 University Press.
- Berger, John. 1972. Ways of Seeing. Penguin.
- Bourdieu, P. 1977. Outline of a Theory of Practice. Cambridge University Press.
- Breiman, L. (2001). Statistical Modeling: The Two Cultures.
- Chollet, F. (2018). Deep Learning with R (1st Edition). 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. 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].
- 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. MIT Press, January 1989.
- Jakobson, Roman. 1971. “On Linguistic Aspects of Translation.” In Selected Writings II: Word and Language, 260–66. 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. Basic Books, May 1974.
- 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. 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. 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 Computer Vision–ECCV 2014, 818–33. Springer.