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Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.
Delves into the spectral bias of polynomial neural networks, analyzing the impact on learning different frequencies and discussing experimental results.
Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.
