Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
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.
