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Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.
Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.
