Moodle@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 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.

For further information please contact cim@warwick.ac.uk or go to  https://warwick.ac.uk/fac/cross_fac/cim/apply-to-study/cross-disciplinary-postgraduate-modules/im931-interdisciplinary-approaches-to-machine-learning/

Syllabus

Week 1: Art, media, and activism—how we got to today (lecture and seminar)

Sets the scene through a brief history of art- and media-activist practices leading up to today, examining the impact of social and technological change; also looks at the impact of networks on art’s autonomy, and the relation between theory and practice, as well as the specificity of the digital as a medium

Week 2: Capital, labour, and value in a digital age (lecture and seminar)

Looks at the ways contemporary political economy has been theorized, together with the possibilities and difficulties that communicative capital poses for organising, popular resistance, and subversive artistic praxis

Week 3: Tactical media, performance, design (lecture and seminar)

Looks at a variety of critical and aesthetic interventions, including electronic civil disobedience, DOS attacks, cybersquatting, Floodnet applications, tactical cartography and visualization, and so forth; also looks at the media activism as performance and aesthetic practice the role of art and design in critical media interventions, especially the role of speculative design fiction

Week 4: The Twitter Revolution (lecture and seminar)

Looks at the role of social media activism in protests and unrest in Moldova, Iran, and the Arab spring examining the relation between twitter and the streets, and the limits and possibilities of digital participation

Week 5: Media squares (lecture and seminar)

Looks at technologies of communication and participation in the Occupy, 15-M and Nuit Debout movements, examining the relation between digital and real-world organising, as well as connections between smart mobs and DIY artistic production

Week 7: Designing media activism (workshop)

In-class crits with pecha kucha presentations of group design projects

Week 8: Digital populisms and far-right co-options (lecture and seminar)—with visiting speaker Dr Paolo Gerbaudo (KCL)

Looks at the use of digital strategies by populist movements today, such as the gilets jaunes, and at the appropriation of critical and tactical-media approaches by the far right, evaluating the political ramifications of these developments for the theory and practice of media activism

Week 9: Digital parties and democratic reformations (lecture and seminar)

Looks at how digital technologies are transforming democratic forms and institutions, with a focus on the potential for social media to reconfigure representation and the relation between leader and base, and on how big data is changing campaigning

Week 10: Whither media activism? (workshop)

A hands-on exploration of online interventions, micropractices, and design fictions that speculate about or advocate for digital futures.

For further information please contact cim@warwick.ac.uk or visit https://warwick.ac.uk/fac/cross_fac/cim/apply-to-study/cross-disciplinary-postgraduate-modules/im933-media-activism/

This module introduces students to the fundamental techniques, concepts and contemporary discussions across the broad field of data science. With data and data related artefacts becoming ubiquitous in all aspects of social life, data science gains access to new sources of data, is taken up across an expanding range of research fields and disciplines, and increasingly engages with societal challenges. The module provides an advanced introduction to the theoretical and scientific frameworks of data science, and to the fundamental techniques for working with data using appropriate procedures, algorithms and visualisation. Students learn how to critically approach data and data-driven artefacts, and engage with and critically reflect on contemporary discussions around the practice of data science, its compatibility with different analytics frameworks and disciplinary, and its relation to on-going digital transformations of society. As well as lectures discussing the theoretical, scientific and ethical frameworks of data science, the module features coding labs and workshops that expose students to the practice of working effectively with data, algorithms, and analytical techniques, as well as providing a platform for reflective and critical discussions on data science practices, resulting data artefacts and how they can be interpreted, actioned and influence society.