Explores decision trees in machine learning, their flexibility, impurity criteria, and introduces boosting methods like Adaboost.
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Covers boosting with a focus on the Adaboost algorithm, forward stagewise additive modeling, and gradient tree boosting.
Covers the structure of finite dimensional algebras and the characterization of semisimple algebras.
Introduces decision trees as a method for machine learning and explains boosting techniques for combining predictors.
