Summary and Schedule
This lesson aims to empower GLAM (Galleries, Libraries, Archives, and Museums) staff by providing the foundation to support, participate in and begin to undertake in their own right, machine learning-based research and projects with heritage collections.
After attending, 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
Prerequisites
None
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Welcome |
Who is this lesson for? What will we be covering in this lesson? What will we not be covering in this lesson? |
| Duration: 00h 05m | 2. Artificial Intelligence (AI) and Machine Learning (ML) in a nutshell |
What is a brief history of the field of (AI) and Machine Learning
(ML)? What do we mean by Artificial Intelligence and Machine Learning? How are they defined? |
| Duration: 00h 05m | 3. Machine Learning Modelling Concepts |
What are models and algorithms? What factors should we consider when choosing a machine learning model? |
| Duration: 00h 05m | 4. What is Machine Learning good at? | What are the tasks where machine learning excels? |
| Duration: 00h 45m | 5. Understanding and managing bias |
What are common types of bias and their effect in machine
learning? At what points can bias enter the machine learning pipeline? Can we manage bias? Some lessons from GLAM |
| Duration: 00h 45m | 6. Applying Machine Learning |
What are the key steps involved in a machine learning project? What skills and people should be involved in a machine learning project? How can machine learning models predictions be utilised by an organization? |
| Duration: 00h 45m | 7. The Machine Learning ecosystem | FIXME |
| Duration: 00h 45m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.