Majorizing Measures, Codes, and Information
Offered By: Simons Institute via YouTube
Course Description
Overview
Explore a thought-provoking lecture on the intersection of information theory and machine learning, delivered by Maxim Raginsky from the University of Illinois, Urbana-Champaign. Delve into the concept of majorizing measures and their applications in coding theory and information processing. Gain insights into how these principles contribute to the development of trustworthy machine learning systems. Discover the connections between mathematical abstractions and practical applications in the field of artificial intelligence during this 36-minute presentation from the Simons Institute's series on Information-Theoretic Methods for Trustworthy Machine Learning.
Syllabus
Majorizing Measures, Codes, and Information
Taught by
Simons Institute
Related Courses
Creating Trustworthy and Ethical Artificial IntelligenceSAP Learning AI and the Law: Implementing Trustworthy AI
Pluralsight Trustworthy AI for Healthcare Management
Politecnico di Milano via Coursera Solana Larsen- Who Has Power Over AI?
Stanford University via YouTube Human-Centered AI: Challenges and Governance in News Automation
Association for Computing Machinery (ACM) via YouTube