Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Explores linear regression, least squares, residuals, and confidence intervals in regression models.
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
