Explore the concept of memorization in machine learning through this insightful 35-minute lecture by Adam Smith from Boston University. Delve into information-theoretic methods for trustworthy machine learning, gaining valuable insights into the challenges and implications of data retention in AI systems. Learn how memorization affects model performance, privacy concerns, and the overall reliability of machine learning algorithms.
Overview
Syllabus
Memorization in Machine Learning
Taught by
Simons Institute
