Lectures related to Evaluation of Binary Classifiers

Supervised Learning Essentials

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

Information Retrieval Basics: Document Frequency and Precision

Introduces information retrieval basics, emphasizing document frequency and precision in evaluating retrieval quality.

Diagnostic Tests: Sensitivity and Specificity

Explains Sensitivity, Specificity, ROC curve, and Precision in diagnostic tests.

Genome-wide Association Studies: Statistical Analysis

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

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GLM: Statistical Hypothesis Testing

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

Machine Learning: Features and Model Selection

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Probability: Quiz

Covers probability scenarios and includes a quiz using Kahoot.

Model Evaluation: K-Nearest Neighbor

Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.