Covers the basics of machine learning, supervised and unsupervised learning, various techniques like k-nearest neighbors and decision trees, and the challenges of overfitting.
Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
