Practice convex optimization as a machine learning prerequisite.

This Gradus course turns convex analysis and optimization methods into concept-level practice with deterministic grading and cumulative review.

Course topics

• Convex sets, affine geometry, cones, and separation
• Convex functions, epigraphs, Jensen reasoning, and function calculus
• Conjugacy, quasiconvexity, log-convexity, and canonical problem forms
• Lagrange duality, perturbation, sensitivity, and semidefinite optimization
• Approximation, statistical estimation, regularized learning, and descent methods

Published package

The remote course ID is convex_optimization. It includes 15 chapters, 61 lessons, and 240 concepts.