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.
Explores the concept of entropy expressed in bits and its relation to probability distributions, focusing on information gain and loss in various scenarios.
Explores Decision Trees, from induction to pruning, emphasizing interpretability and automatic feature selection strengths, while addressing challenges like overfitting.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
