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Explores AdaBoost with decision stumps, discussing error rules, stump selection, and the need for a bias term.
Explains Adaboost algorithm for building strong classifiers from weak ones, with a focus on boosting methods and face detection.
Covers boosting with a focus on the Adaboost algorithm, forward stagewise additive modeling, and gradient tree boosting.
Covers weak learners in boosting, AdaBoost algorithm, drawbacks, simple weak learners, boosting variants, and Viola-Jones Haar-Like wavelets.
Explores decision trees in machine learning, their flexibility, impurity criteria, and introduces boosting methods like Adaboost.
Introduces decision trees as a method for machine learning and explains boosting techniques for combining predictors.
Delves into orchestration graphs, transition probabilities, and learning analytics for predicting student states.
Explores postmortem memory analysis using news and social media data, investigating biographic correlates and memory patterns.
Introduces Multi-Layer Perceptrons (MLPs) and covers logistic regression, reformulation, gradient descent, AdaBoost, and practical applications.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.
Explores the analysis of postmortem memory of public figures in news and social media, uncovering significant insights into memory formation.
