Generalization Error

This lecture covers the concept of generalization error in machine learning, explaining how it relates to the distribution of data and the hypothesis used. It discusses the population risk, empirical risk, and learning algorithms, providing insights into the bounds on generalization error. The instructor also delves into the setup and problem statement, emphasizing the importance of understanding the data and the hypothesis to minimize errors.