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This lecture introduces machine learning techniques for nonlinear regression, focusing on forecasting trends in complex data sets. The instructor explains the concept of regression, the importance of nonlinear regression in machine learning, and its applications in various fields such as control, optimization, and finance. Through examples, the lecture demonstrates how nonlinear regression can predict complex temporal data and encapsulate trends with variable time windows. The instructor also discusses the importance of confidence in predictions and the robustness of techniques to signal noise ratio and bias noise.