Lecture 1 | Convex Optimization I (Stanford)
Stanford · 2 HN recommendations
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The introductory lecture of Stephen Boyd's Stanford Convex Optimization I course (EE 364A). It outlines convex sets, functions and problems, and the course's coverage of least-squares, linear and semidefinite programming, duality and interior-point methods.
Why HN recommends it: HN recommends Boyd's lectures as a fantastic, often funny follow-up to linear algebra and as a step in machine learning study paths.
“These lectures are fantastic after you've mastered the basics of linear algebra” Read on HN ↗