Data Representations and ProcessingDiscusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.
Cross-validation & RegularizationExplores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.
Kernel Methods: Machine LearningCovers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.
Probabilistic Linear RegressionExplores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Nonlinear ML AlgorithmsIntroduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
