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This lecture covers the concepts of generalization, model selection, and validation in machine learning. It discusses the challenges of verifying model performance, choosing hyperparameters, and understanding generalization error. Topics include ridge regression, neural network architectures, empirical and training errors, data splitting, and cross-validation techniques. The lecture also explores concentration inequalities, Hoeffding's inequality, and the relationship between empirical and true risks in model selection.
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