Linear Algebra… doesn’t it sound so impressive?
When I was in my last year of high school, we had three options for math courses: calculus, statistics (called finite for some reason) and linear algebra. Honest to god, I skipped LA because it sounded so daunting (and wasn’t a strict prerequisite for any university programs I applied to).
So often intimidating jargon masks very simple procedures and concepts.
Well, I’m learning LA over breakfast today because matrix multiplication is the fastest way of comparing linear regression functions’ effectiveness (that’s what we’re hinting at, anyway). Matrix multiplication is actually so simple I’m not even going to bother with notes.
What’s interesting to me is why it’s useful in this context. Quite simply, it’s useful because somebody (somebodies) spent a bunch of time building super-fast matrix multiplication functionality in every imaginable programming language.
Now, I don’t know why people have designed super-optimal implementations of matrix multiplication, but it’s a pretty awesome public good. Did they do this before Machine Learning made selecting from among various linear regression algorithms was a problem to solve?
Realistically, it was probably a bunch of kids looking to do an awesome PhD dissertation: why not build a super-optimized matrix multiplication library?
Learning by solving problems. That’s what it’s all about. Hat Tip to Alan Kay.
Not Unreasonable 