Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn about linear classifiers and their role as an expressive hypothesis class in this machine learning lecture. Explore key concepts including augmented versions, monotone conjunctions, linear functions, one-dimensional vectors, feature transformations, and error limits. Gain practical insights into how these components work together to create effective linear models for classification tasks. Delve into detailed mathematical foundations and theoretical frameworks that underpin modern machine learning applications.