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Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.
Covers the Nearest Neighbor search algorithm and the Johnson-Lindenstrauss lemma for dimensionality reduction, exploring preprocessing techniques and locality-sensitive hashing.
Explores the nearest neighbor classifier method, discussing its limitations in high-dimensional spaces and the importance of spatial correlation for effective predictions.
