15/20/30 CATS (7.5/10/15 ECTS)

Term 2

Module Convenor - Dr Michael Castelle

Outline

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, 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.

Assessments

• • 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)

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 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.

CIM Students

• Please first discuss your optional module choices with you personal tutor during the personal tutor meetings and get their approval
• Then complete and submit the optional module choice webform available in the CIM welcome page
• The webform opens on 30th September at 14:00 BST and closes on 1st October at 15:00 BST
• If there are any queries, please get in touch with Gheerdhardhini (PG Coordinator) via cim@warwick.ac.uk 

External Students

• All external students - Please contact the CIM PG Coordinator (Gheerdhardhini) via email (cim@warwick.ac.uk), to request your optional module choice by Week 1 : Wednesday, 7th October, 17.00 BST.

PLEASE NOTE

• Please be advised that you may be expected to have access to a laptop for some of these courses due to software requirements; the Centre is unable to provide a laptop for external students.
• Please be advised that some modules may have restricted numbers and places are allocated according to availability.
• Please note that a request does NOT guarantee a place on the module and is subject to availability.
• Gaining permission of a member of CIM teaching staff or a member of staff from your home department or filling in the eVision Module Registration (eMR) system with the desired module does NOT guarantee a place on that module.
• Requests after the specified deadline will not be considered.
• CIM PG Coordinator will get back confirming your place in the module by 2nd October, Friday (For CIM students).
• For external students - Only after confirmation of a place from CIM PG Coordinator can students’ or their home departments confirm their registration on eVision/MRM. Registrations by students who have not received confirmation of a place from CIM will be rejected via the system.

NOTE – The above-mentioned registration deadline also applies to the CIM optional modules running in Term 2. We will consider registrations again in the first week of Term 2, but only in relation to modules where there is availability.

We are normally unable to allow students (registered or auditing) to join/leave the module after the second week of it commencing.