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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 the Gaussian conditional model for linear regression and the properties of Gaussian data, illustrated with the example of kidney stone treatment comparison.
Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.