The course provides an introduction to machine learning methods and workflows for life science research. It introduces the full end-to-end machine learning (ML) workflow, from data preprocessing and feature engineering to model training, evaluation, interpretation, and reproducible reporting, with a focus on the analysis of complex, high-dimensional biological data. Participants explore biological datasets using unsupervised methods such as dimensionality reduction and clustering, and build predictive models using supervised approaches including linear and tree-based models. Methods for multi-omics integration, including partial least squares (PLS), are introduced, and regularisation techniques for high-dimensional data, such as ridge, lasso, and elastic net, are also covered.

Content

• Overview of the machine learning workflow
• Dimensionality reduction methods such as PCA and UMAP
• Unsupervised learning and clustering methods
• Supervised learning models, including tree-based models
• Partial least squares (PLS) for multi-omics integration
• Regularization methods.
• Model training, evaluation and validation strategies
• Model interpretation and explainable machine learning methods