CDH Methods Workshop: Machine Learning Systems: a critical introduction

New

Dr Anne Alexander, Cambridge Digital Humanities

Places are limited and participants must complete this form in order to participate in addition to booking online. We will write and confirm your participation by email. Bookings will remain open until 10am, 11 October 2021; However, participants are encouraged to apply early as demand is likely to be high.

This online 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. It is designed as a preparatory session for potential applicants to our Interaction with Machine Learning Guided Project which will run in Lent Term 2022 in collaboration with the Department of Computer Science and Technology. However, it can also be booked as a standalone session.

This course is open to graduate students and staff at the University of Cambridge. Early career researchers are particularly encouraged to apply.

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/)

Number of sessions: 2

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

In addition to the two live sessions, participants will be encouraged to work through practical exercises in image classification and/or text generation in their own time. This experimental work will take an additional 2-3 hours to complete.