Explores sources of unfairness in machine learning, the importance of fairness metrics, and evaluating model predictions using various fairness metrics.
Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.
Covers the concept of biometrics, the process of enrolling and verifying biometrics, and the importance of balancing false positives and false negatives.
Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.
