Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.
Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
