Moodle@Warwick

This interdisciplinary postgraduate module offers an in-depth introduction to Design Thinking as research and practice and focuses on its uses in the field of "social change".

Design Thinking for Social Impact is an effective way to gain insights into pressing human problems and build the skills required to develop innovative and sustainable solutions. 

Syllabus

Week 1 – Introduction: Genealogies of the Interface

Week 2 – The Interface as Socio-Technical Assemblage

Week 3 – What is a User?

Week 4 – [Graphics] – The Operational Image

Week 5 – [Workflow] – Governance of Actions

Week 6 – [Processing] – Time and Cognition

Week 7 – [Analytics] – Trace Data, Optimization and Social Media Platforms

Week 8 – [Storage] – From Web Archives to Digital Folklore

Week 9 – Conclusion: The Mediation of Behaviour 

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/im923-user-interface/

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/

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/

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.