Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of model complexity and different cross-validation methods.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers overfitting, model selection, validation methods, kernel functions, and SVM concepts.
Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.
Covers the concept of linear regression, including polynomial regression and hyperparameters selection.
