Diagnostic Tests: Sensitivity and Specificity

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

Understanding ROC Curves

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

Performance Criteria: Confusion Matrix, Recall, Precision, Accuracy

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

Linear Regression: Basics

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

Performance Evaluation: Bootstrap and Performance Metrics

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

Evaluation Protocols

Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.

Generalized Linear Regression: Classification

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

Genome-wide Association Studies: Statistical Analysis

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

Machine Learning in Human Rights: HURIDOCS

Explores machine learning in human rights, focusing on defining goals, handling false positives and negatives, and ensuring transparency and trust.

Information Retrieval Basics: Document Length and Normalization

Explores document length, normalization, bias compensation, and retrieval model evaluation in information retrieval.