Explores clustering methods for partitioning data into meaningful classes when labeling is unknown, covering K-means, dissimilarity measures, and hierarchical clustering.
Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.