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This lecture covers stochastic subgradient methods, composite convex minimization, and sparse regression in generalized linear models. It introduces proximal operators, proximal gradient methods, and Frank-Wolfe methods. The instructor discusses the convergence analysis of these methods and their applications in various optimization problems.