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Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Explores Transductive Support Vector Machine for semi-supervised clustering, aiming for zero error on labeled points and well-separated unlabeled points.