Introduces optimization basics, covering norms, convexity, differentiability, and more, with a focus on metrics, vector norms, matrix norms, and continuity.
Covers optimization basics, including metrics, norms, convexity, gradients, and logistic regression, with a focus on strong convexity and convergence rates.
Introduces functional analysis, distribution theory, topological vector spaces, and linear operators, emphasizing their importance in engineering applications.
