I'm currently taking an advanced Linear Algebra course on Linear Dynamic Systems, and we're covering things like Least-Squares Approximation, Multi-Objective Least Squares, finding the Least-Norm Solution, the Matrix Exponential, and Autonomous Linear Dynamic Systems.
This is all very overwhelming to a guy whose only taken basic linear algebra before (Null-space, column-space, Eigenvectors, etc.), and I'm having a tough time figuring out how this relates to machine learning and data mining. This all makes perfect sense for electrical engineering, circuits, population dynamics, but I have no idea how any of this plays a role in ML.
Can someone enlighten me on how the concepts above help make you a better ML person?
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