02 — Unsupervised Learning
Learning structure from unlabelled data: discovering clusters, reducing dimensionality, estimating densities, and learning compact representations.
Sublayers
01 — Clustering
K-means, DBSCAN, hierarchical clustering, Gaussian mixture models.
02 — Dimensionality Reduction
PCA, t-SNE, UMAP, random projections, factor analysis.
03 — Density Estimation
KDE, GMMs, normalising flows, kernel density approaches.
04 — Representation Learning
Autoencoders, VAEs, contrastive learning, self-supervised pre-training.