Evaluation of Binary Classifiers

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Linear Regression: Basics

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

Generalized Linear Regression: Classification

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

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

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.

Evaluation Protocols

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

Performance Evaluation: Bootstrap and Performance Metrics

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

Fairness in Machine Learning

Explores sources of unfairness in machine learning, the importance of fairness metrics, and evaluating model predictions using various fairness metrics.

Quantifying Performance: Misclassification and F-Measure

Covers quantifying performance through true positives, false negatives, and false positives in machine learning.

Classification: Decision Trees and kNN

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