Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.
Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.
Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.
Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
