[mynameisash]

I took linear algebra about 20 years ago as a requirement for my CS degree. I had no clue whatsoever what it was for beyond just "solving systems of equations" - whatever that meant. The prof didn't impress upon us how it underpins all of machine learning (which, mind you, wasn't as much of a hot topic then that it is now). Our attempts at grokking its importance were largely met with a kind of "because it's important" dismissal.

I didn't do great in the course, and when it was done, I was pretty happy this useless subject was behind me.

It wasn't until several years into my profession that I learned how important linear algebra truly is. I now work very closely with tons of data scientists, and I still feel like I should go back and learn it better.

[flashgordon]

Same thing happened to me. Yes ml wasn't a hot area then (20 years ago) but computer graphics, linear regression (ie old school statistics used for supply chain), signal processing for audio and video were all got them but their applicability was never pointed out let alone emphasized. I was just lucky that I was interested in these areas on my own so the linkages thankfully "clicked". Is it me or did profs back then just do research and teaching in a bubble?