Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
