Boosting: Adaboost AlgorithmCovers boosting with a focus on the Adaboost algorithm, forward stagewise additive modeling, and gradient tree boosting.
Advanced Machine Learning: BoostingCovers weak learners in boosting, AdaBoost algorithm, drawbacks, simple weak learners, boosting variants, and Viola-Jones Haar-Like wavelets.
Adaboost: Boosting MethodsExplains Adaboost algorithm for building strong classifiers from weak ones, with a focus on boosting methods and face detection.
Orchestration GraphsDelves into orchestration graphs, transition probabilities, and learning analytics for predicting student states.
Advanced Machine Learning: BaggingExplores ensemble learning methods like Bagging and Boosting to improve model performance through aggregation and the selection of unstable models.
Generalization in Deep LearningDelves into the trade-off between model complexity and risk, generalization bounds, and the dangers of overfitting complex function classes.
Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
