We are currently unpacking manifold learning, a modern area in applied mathematics and machine learning. None of us claim expertise yet; instead we divide the field into smaller themes, teach each other, and synthesise what we learn.
Through this process we aim for a shared, deep understanding that balances rigorous mathematics with computational experimentation.
Mathematical Landscapes
Differential geometry, topology, and linear algebra form the language we need to reason about manifolds.
Algorithms in Context
We explore PCA, Isomap, LLE, t-SNE, UMAP, and related methods to see how geometry informs data analysis.
Weekly Topics
Rotating presenters curate references, prepare slides, and guide the group through the week's material.
Learning Artefacts
Lecture notes, slides, and documents are working materials drawn from the literature—they support study rather than present original research.