Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.
Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.
Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.
Explores out-of-sample validation and the methodology of cross-validation for testing predictive models.
Introduces machine learning basics, including data collection, model evaluation, and feature normalization.
