Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.
Emphasizes the significance of careful cross-validation in deep neural networks, including the split of data and the concept of K-fold cross-validation.
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Explores out-of-sample validation and the methodology of cross-validation for testing predictive models.
