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Introduces Support Vector Clustering (SVC) using a Gaussian kernel for high-dimensional feature space mapping and explains its constraints and Lagrangian.
Covers the evaluation of clustering methods, including K-means clustering and the use of evaluation metrics to determine the optimal number of clusters.
Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.
