Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the intersection of privacy, stability, and online learning in this 37-minute lecture by Mark Bun from Boston University. Delve into motivating questions surrounding non-private classification, focusing on learning one-dimensional thresholds. Examine the sample complexity of learning and private learning, characterizing private sample complexity and learnability. Investigate online learning, the Littlestone dimension, and compare mistake-bounded learning with differential privacy. Part of the "Workshop on Differential Privacy and Statistical Data Analysis" at the Fields Institute, this talk provides insights into crucial aspects of data privacy and machine learning.