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Explores robust and resistant methods in linear models, emphasizing the importance of handling extreme observations and the implications of robustness in regression models.
Explores the importance of causality for robust machine learning, covering ideal datasets, missing data problems, graphical models, and interference models.
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.