Fairness in Machine Learning

Related lectures (32)

Page 1 of 4

Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.

Explores performance criteria in supervised learning, emphasizing precision, recall, and specificity in model evaluation.

Discusses the evaluation of binary classifiers, including recall, sensitivity, specificity, ROC curves, and performance measures.

Explores machine learning model evaluation using leave-one-out, bootstrap, and performance metrics like recall and precision.

Covers probability scenarios and includes a quiz using Kahoot.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Explores statistical hypothesis testing, error types, thresholding, and multiple comparisons in GLM.

Explains the statistical analysis of Genome-wide Association Studies and the importance of adjusting for multiple testing.

Covers statistical hypothesis testing and error types in neural signals processing.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.