Fairness in Machine Learning

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Related lectures (32)

Page 3 of 4

Diagnostic Tests: Sensitivity and Specificity

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

Quantifying Performance: Misclassification and F-Measure

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

Hypothesis Testing: Statistics Overview

Provides an overview of hypothesis testing, p-values, Wald test, and non-parametric statistics.

Evaluation Protocols

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

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Biometrics & Tokens: Advantages, Authentication, & Adversarial Reasoning

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

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Protein Mass Spectrometry and Proteomics: Search Engines and Database Algorithms

Explores protein mass spectrometry, search engines, and quantification methods in proteomics.

Classification: Decision Trees and kNN

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

Machine Learning Biases

Explores machine learning basics, adversarial challenges, biases, distributional shift, and deployment complexities.