Explores decision trees in machine learning, their flexibility, impurity criteria, and introduces boosting methods like Adaboost.
Explores AdaBoost with decision stumps, discussing error rules, stump selection, and the need for a bias term.
Explores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.
Covers boosting with a focus on the Adaboost algorithm, forward stagewise additive modeling, and gradient tree boosting.
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Introduces decision trees as a method for machine learning and explains boosting techniques for combining predictors.
