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Explores the trade-off between complexity and risk in machine learning models, the benefits of overparametrization, and the implicit bias of optimization algorithms.
Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.
Delves into the challenges and opportunities of machine learning in credit risk modeling, comparing traditional statistical models with machine learning methods.
Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.
