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Complexity: Approximation-Estimation Trade-off

Explores the control of complexity in hypothesis spaces and the trade-off between approximation and estimation in risk decomposition.

Generalization Theory

Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.

Bias-Variance Trade-off

Explores the impact of model complexity on prediction quality through the bias-variance trade-off, emphasizing the need to balance bias and variance for optimal performance.

Bias-Variance Tradeoff in Machine Learning

Explores the Bias-Variance tradeoff in machine learning, emphasizing the balance between bias and variance in model predictions.

Machine Learning Fundamentals: Overfitting and Regularization

Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.