STATS 202B: Matrix Algebra and Optimization
Graduate course, UCLA, 2023
This course introduces students to algorithms and their theoretical underpinnings extensively used in modern machine learning. It covers
- first order methods, including gradient descent and its variants;
- conditions for optimality for convex;
- constrained optimization problems and basics of duality theory;
- challenges posed by non-convex problems.
This course is offered in Winter Quarter 2025, Winter Quarter 2024, Winter Quarter 2023