Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.
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.
Introduces hierarchical and k-means clustering methods, discussing construction approaches, linkage functions, Ward's method, the Lloyd algorithm, and k-means++.