Lectures related to Fairness in Machine Learning

Evaluation of Binary Classifiers

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

Probability: Quiz

Covers probability scenarios and includes a quiz using Kahoot.

GLM: Statistical Hypothesis Testing

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

Statistical Hypothesis Testing & Error Types

Covers statistical hypothesis testing and error types in neural signals processing.

Generalized Linear Regression: Classification

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

Authentication - Biometrics

Covers the concept of biometrics, the process of enrolling and verifying biometrics, and the importance of balancing false positives and false negatives.

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.

Hypothesis Testing in Statistics

Delves into hypothesis testing, covering test statistics, critical regions, power functions, p-values, multiple testing, and non-parametric statistics.

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.

Machine Learning Biases

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

Hypothesis Testing: State of Nature

Explores hypothesis testing, emphasizing the state of nature and the importance of choosing the most powerful test.

Understanding ROC Curves Log in to Mediaspace to watch this video

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

Performance Criteria: Confusion Matrix, Recall, Precision, Accuracy Log in to Mediaspace to watch this video

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

Performance Evaluation: Bootstrap and Performance Metrics Log in to Mediaspace to watch this video

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

Linear Regression: Basics Log in to Mediaspace to watch this video

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

Genome-wide Association Studies: Statistical Analysis Log in to Mediaspace to watch this video

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

Data Summarization: Minhashing and Locality-Sensitive Hashing Log in to Mediaspace to watch this video

Explores Jaccard similarity, minhashing, and locality-sensitive hashing for data summarization.

Data Compression: Privacy-Preserving Techniques Log in to Mediaspace to watch this video

Explores privacy-preserving data management and data summarization techniques for finding similar items.

Supervised Learning Essentials Log in to Mediaspace to watch this video

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