This bootcamp explores the wide world of mathematical optimization, specifically for applications in machine learning, control theory, and inverse problems. This closely follows the textbook "Optimization: A Bootcamp for Machine Learning, Inverse Problems, and Control" (https://www.amazon.com/Optimization-Bootcamp-Machine-Learning-Problems/dp/1009755862)
Curated by: Steve Brunton (44 videos)
In this lecture I give an overview of the goals, topics, and structure to be presented in the Optimization Bootcamp lecture series. https://www.amazon.com/Optimization-Bootcamp-Machine-Learning-Problems/dp/1009755862 This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company. %%% CHAPTERS %%% 00:00 Intro 01:01 What is an Optimization Problem? 03:10 Applications of Optimization 07:52 Properties of the Objective & Constraint Functions 11:30 Convexity & Optimization 13:45 Linear & Quadratic Programming 15:51 Non-Convex Optimization 16:49 Series Overview & Structure 21:34 Outro