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This lecture by the instructor covers the role of computation in the mathematics of data, focusing on iterative descent methods, unconstrained minimization, maximum-likelihood estimators, approximate vs. exact optimality, and the basic principles of descent methods. The lecture delves into the challenges faced by iterative optimization algorithms, the conditions for local descent directions, and the basic iterative strategy for optimization algorithms. It also discusses the concepts of local steepest descent direction, stationarity, and the geometric interpretation of descent methods.