Information-Theoretic Methods for Fair Risk Minimization
Offered By: Simons Institute via YouTube
Course Description
Overview
Explore information-theoretic approaches to fair risk minimization in machine learning with Ahmad Beirami from Google Research. Delve into advanced concepts and techniques for developing trustworthy AI systems, focusing on how information theory can be applied to address fairness concerns in risk assessment and decision-making processes. Gain insights into cutting-edge research that aims to enhance the reliability and equity of machine learning models across various applications.
Syllabus
Information-Theoretic Methods for Fair Risk Minimization
Taught by
Simons Institute
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent