Covers the fundamentals of convex optimization, including mathematical problems, minimizers, and solution concepts, with an emphasis on efficient methods and practical applications.
Explores optimization methods, including convexity, gradient descent, and non-convex minimization, with examples like maximum likelihood estimation and ridge regression.
