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This lecture covers Monte Carlo integration through correlated sampling, focusing on the motivation behind using this method, the integration process, and the scaling of errors. It also discusses the performance of supercomputers in this context and compares Monte Carlo methods with conventional schemes. The lecture concludes with a detailed analysis of error scaling for grid integration in multiple dimensions.