ML1 - Basic course: S5
Demystifying machine learning for medical data analysis
Content
Past, present and future of ML applied to Medical Data Analysis. The field of ML is very broad; and an emphasis will be put on clarifying concepts and simplifying them for a general audience. Concepts include:
A brief overview of the machine learning cycle
Data preparation
Splitting data into train, test and validation sets
Training a ML model
A taxonomy of ML models, including the most prominent families of ML models
Cross-validation and external validation
Bringing models to production (model deployment)
Links between ML and conventional tools in statistics (the past)
A selection of medical data analysis studies/articles applying ML (the present)
End-to-end pipelines and the promises of ML; how the ML cycle will be embedded in medical data management centers (the future)
Based on this general introduction, participants will be encouraged to suggest at least one current task in their own field of research where ML could be applied.
Learning outcomes
This session is aimed at a general audience and provides a high-level overview of ML. At the end of the session, attendants should have a ‘demystified’ view of ML and, hopefully, they will be encouraged to explore the potential of ML in their own projects. Thus, the main outcomes are:
A qualitative understanding of the field of ML
A personalized list of items/tasks related to participant’s projects where ML could be applied.
Materials
Pre-recorded video (1h): https://youtu.be/rnQAUeOFl3E
See Moodle for more!
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