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This lecture covers linear transformations, joint continuous densities, and the convolution of random variables. It explains how to find the joint density of transformed random variables and the PDF of the sum of independent random variables. The instructor demonstrates the use of Jacobians in transformations and provides examples to illustrate these concepts.