Lessons
1
Unsupervised learning: what are we optimizing?
2
K-means clustering
3
K-means convergence and limitations
4
Gaussian mixture models: soft clustering
5
The EM algorithm
6
PCA: principal component analysis
7
Eigendecomposition for PCA
8
Explained variance and choosing dimensions
9
t-SNE and UMAP: non-linear reduction
10
Self-supervised learning: predicting your own data