Covers the role of models and data in statistical learning and optimization formulations, with examples of classification, regression, and density estimation problems.
Introduces optimization basics, covering norms, convexity, differentiability, and more, with a focus on metrics, vector norms, matrix norms, and continuity.
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.
