Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
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.
Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.
