Our aim with this lesson is to empower GLAM (Galleries, Libraries, Archives, and Museums) staff with the foundation to support, participate in and begin to undertake in their own right, machine learning based research and projects with heritage collections.
After following this lesson, learners will be able to:
- Explain and differentiate key terms, phrases, and concepts associated with AI and Machine Learning in GLAM
- Describe ways in which AI is being innovatively used in the cultural heritage context today
- Identify what kinds of tasks machine learning models excel at in GLAM applications
- Identify weaknesses in machine learning models
- Reflect on ethical implications of applying machine learning to cultural heritage collections and discuss potential mitigation strategies
- Summarise the practical, technical steps involved in undertaking machine learning projects
- Identify additional resources on AI and Machine Learning in GLAM
We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.
We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.
Please see the current list of [issues][# TODO] for ideas for contributing to this
repository. For making your contribution, we use the GitHub flow, which is
nicely explained in the chapter Contributing to a Project in Pro Git
by Scott Chacon.
Look for the tag 
Current maintainers of this lesson are
- Mark Bell
- Nora McGregor
- Daniel van Strien
- Mike Trizna
A list of contributors to the lesson can be found in AUTHORS
To cite this lesson, please consult with CITATION