Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.
Explores clustering methods for partitioning data into meaningful classes when labeling is unknown, covering K-means, dissimilarity measures, and hierarchical clustering.
Covers quantization of probability distributions, statistical k-means clustering, mean estimation, robust clustering methods, and open research questions.