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Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.
Explores the time-varying Kalman filter, state estimation, challenges in conditioning on measured outputs, and the importance of affine transformations.
Explores independence between sigma-algebras and measurable functions, emphasizing countably additive measures and their role in defining independence.
