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Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
Covers maximum likelihood estimation to estimate parameters by maximizing prediction accuracy, demonstrating through a simple example and discussing validity through hypothesis testing.
