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Introduces linear regression basics from an empirical risk minimization perspective, covering the square loss, data preprocessing, and gradient computation.
Explores the impact of gradient noise on optimization algorithms, focusing on smooth and nonsmooth risk functions and the derivation of gradient noise moments.
Explores stochastic optimization in portfolio management, emphasizing decision criteria for uncertain objectives and the concept of conditional value-at-risk.
