Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Discusses the evaluation of binary classifiers, including recall, sensitivity, specificity, ROC curves, and performance measures.
Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
