Explore the capabilities and expressiveness of linear classifiers in this 43-minute machine learning lecture, which delves into the theoretical foundations and practical implications of linear classifier hypothesis classes. Gain deeper insights into how these fundamental models represent and separate data, building upon previous discussions of linear classification methods. Access supplementary materials and detailed lecture notes through the course website for a comprehensive understanding of linear models in machine learning.