Fairness in Machine Learning

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Related lectures (32)

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Data Summarization: Minhashing and Locality-Sensitive Hashing

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

Multiple Testing Problem

Explores the challenges of multiple testing in genomic data analysis, covering error rate control, adjusted p-values, permutation tests, and pitfalls in hypothesis testing.

Authentication - Biometrics

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

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Data Compression: Privacy-Preserving Techniques

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

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.

Supervised Learning Essentials

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

Polynomial Regression: Overview

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Hypothesis Testing in Statistics

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

Linear Models for Classification: Multi-Class Extensions

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