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This lecture covers the concept of binary classification by regression, focusing on decision functions and cost functions. It explains how to regress labels to determine a decision function, the quality of this approximation, and different loss functions such as 0/1 cost, logistic cost, quadratic cost, and hinge error. The instructor also discusses the implications of these cost functions on linear regression for binary classification.