#118 overall
EE263 Introduction to Linear Dynamical Systems
Stanford University
4 recommenders · 4 threads · recommended in 3 years, 2011–2020 · score 3.7
Stanford graduate course on linear dynamical systems taught by Stephen Boyd, covering how real problems are formulated so computers can solve them, with topics like matrix rank, norms, and singular values applied to system stability and robustness.
Commenters call it really good and praise Boyd as a very entertaining, clear lecturer who shows how to turn real problems into computable form.
Mathematicsadvancedfreevideo lectures#linear algebra#dynamical systems#singular values#stability#boyd
Recommendations by year
20062026
Had similar intentions half a year ago, it really clicked for me after watching the linear dynamical systems lecture by Boyd. How rank of a matrix, matrix norm and singular values relate was an eye opener. Thereafter it was easy to connect singular values and the stability/robustness of a system intuitively. Great teacher, I can only recommend :)
https://www.youtube.com/playlist?list=PL06960BA52D0DB32B
After reading this book (or during), take a look at the author's (Boyd) video lectures on linear dynamical systems: https://see.stanford.edu/Course/EE263/
There is a lot of overlap between the book and the course.
The (free, open) stanford class ee263: Linear Dynamical Systems is really good. It concentrated on how to take real problems and fit them into a form where a computer can solve them in what appears to be complete magic. Plus Stephen Boyd is a very entertaining lecturer. This article reminds me a lot of his asides.
Link to lectures and materials: http://see.stanford.edu/see/courseinfo.aspx?coll=17005383-19...
Start with the MIT linear algebra course (18.06) by Gilbert Strang and Stanford course on linear dynamical systems (EE263) by Stephen Boyd. Then move on to Boyd's course on convex optimization (EE364). Lectures for all of these are on youtube.
Do not try to read any books on "machine learning" (most of which are a total mess) before you have this background or you will just end up hopelessly confused.
For a broad(er) perspective on AI, checkout UC Berkeley's CS188 http://inst.eecs.berkeley.edu/~cs188. I think it's a good entry course into AI. One advantage is that the math requirement is not as high as Stanford's CS229 or EE263 (both of which are fantastic courses to be clear, but are easier to appreciate with the correct background).