Maximum Likelihood Estimator: Expression and Gradient
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Description
This lecture covers the derivation of the maximum likelihood estimator expression for x, given knowledge of b and a, in the form arg min (f(x, a), b). It also explores the gradient of the loss function L(x) with respect to x.
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Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
