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Cambridge Digital Humanities

Cambridge Digital Humanities course timetable

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Tue 21 Mar – Mon 24 Apr

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March 2023

Tue 28
CDH Methods | Best Practices in Coding for Digital Humanities new [Places] 09:00 - 13:00 Sidgwick Site, Alison Richard Building S3

Convenor: Mary Chester-Kadwell -  Lead Research Software Engineer, Cambridge Digital Humanities

Please note this workshop has limited spaces and an application process in place. Application forms should be completed by noon, Sunday, 12 March 2023. Successful applicants will be notified by the end-of-day Tuesday, 14 March 2023. 

This course introduces best practices and techniques to help you better manage your code and data, and develop your project into a usable, sustainable, and reproducible workflow for research.

Developing your coding practice is an ongoing process throughout your career. This intermediate course is aimed at students and staff who use coding in research, or plan on starting such a project soon. We present an introduction to a range of best practices and techniques to help you better manage your code and data, and develop your project into a usable, sustainable, and reproducible workflow. All the examples and exercises will be in Python.

If you are interested in attending this course, please fill in the application form. Please ensure you are logged onto your University Google account to access the form further help here

April 2023

Mon 24
CDH Methods | Machine Learning Systems: a critical introduction new [Places] 13:00 - 17:00 Cambridge University Library, IT Training Room

This in-person workshop will provide an accessible, non-technical introduction to Machine Learning systems, aimed primarily at graduate students and researchers in the humanities, arts and social sciences.

Key topics covered in the sessions will include:

  • Situating Machine Learning in the longer history of Artificial Intelligence
  • Machine Learning system architectures
  • The challenges of dimension reduction, classification and generalisation
  • Sources of bias and problems of interpretation
  • Machine Learning applications and their societal consequences

During the session participants will be encouraged to work through practical exercises in image classification. No prior knowledge of programming is required. Participants wishing to run the experiments for themselves will need access to a laptop, but no special software is required, just an up-to-date web browser and an internet connection. We will be using Google Colab for the text generation experiments which you have access to via your Raven log-in. The image classification experiments will require a GitHub account ([sign up here https://github.com/])