Clustering: Theory and PracticeCovers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Graph Coloring IIExplores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.
Unsupervised Behavior ClusteringExplores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.