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Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.
Discusses the evaluation of binary classifiers, including recall, sensitivity, specificity, ROC curves, and performance measures.
Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.
Introduces machine learning basics, including data collection, model evaluation, and feature normalization.
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
