Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Explores sources of unfairness in machine learning, the importance of fairness metrics, and evaluating model predictions using various fairness metrics.
