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Explores the Gaussian conditional model for linear regression and the properties of Gaussian data, illustrated with the example of kidney stone treatment comparison.
Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.
Explores regression analysis, kinetics modeling, parameter estimation, and atmospheric ozone concentration modeling using linear relationships and scatterplots.