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.
Metrics for ClassificationCovers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.
Supervised Learning in Financial EconometricsExplores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.