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.
Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.
Explores generalization in machine learning, focusing on underfitting and overfitting trade-offs, teacher-student frameworks, and the impact of random features on model performance.