I started taking Gilbert Strang's "new" course, "Matrix Methods In Data Analysis, Signal Processing, And Machine Learning" (18.065), in my free time after heari

I started taking Gilbert Strang's "new" course, "Matrix Methods In Data Analysis, Signal Processing, And Machine Learning" (18.065), in my free time after hearing about his retirement news. https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/pages/syllabus/ https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-an... It's been 17 years since I took 18.06 with Professor Strang and I've always been disappointed with my grasp of Linear Algebra, especially as it's become more and more of an important and applicable topic. It very much stopped then and I've never felt terribly confident in it. The course is great because it starts with a 6 lecture refresher that brings you back up to speed on the basics of Linear Algebra before diving into the meat of the course. To avoid being completely 100% on the Strang bandwagon, his lectures are a little off the cuff and could probably benefit from some visual aids or interactive tools outside of white boards to help students visualize what's really going on with these matrices, but the current version of the course (https://github.com/mitmath/18065/tree/main/psets https://github.com/mitmath/18065/tree/main/psets) has a bunch of Julia IJupyter Notebook assignments that help you get down and dirty with what you're really trying to learn - the application of Linear Algebra.