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Covers gradient descent methods for convex and nonconvex problems, including smooth unconstrained convex minimization, maximum likelihood estimation, and examples like ridge regression and image classification.
Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.
Explores optimization methods, including convexity, gradient descent, and non-convex minimization, with examples like maximum likelihood estimation and ridge regression.
