Lectures related to Diagnostic Tests: Sensitivity and Specificity

Delves into the application of machine learning in enhancing human rights documentation and advocacy for organizations like HURIDOCS.

Explores biometrics, tokens, authentication processes, and adversarial reasoning in security engineering.

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

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Covers hypothesis testing steps and introduces ROC curves for test comparison.