Optimization for Machine Learning
Machine learning poses data driven optimization problems. Computing the function value and gradients for these problems is challenging because they often involves thousands of variables and millions of training data points. This can often be cast as a convex optimization problem. Therefore, a lot of recent research has focused on designing specialized optimization algorithms for such problems. In this talk, I will present a high level overview of a few such algorithm that were recently developed. The talk will be broadly accessible and will have plenty of fun pictures and illustrations!
See other lectures at Purdue MLSS Playlist: