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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.
Explores graphical model learning with M-estimators, Gaussian process regression, Google PageRank modeling, density estimation, and generalized linear models.
Introduces the basics of linear regression, interpreting coefficients, assumptions, transformations, and 'Difference in Differences' for causal analysis.
