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Maybe "eigenvectors" is a bad example, because it's a pretty foundational linear algebra concept. But there is a threshold where it stops being a test of found

• whimsicalism 5y ago
  Having recently completed an MLE interview loop successfully at a top company, I'm wondering where you are getting asked complicated linear algebra questions in interview?
  • fault1 5y ago
    Hopefully you aren't equating "eigenvectors" to "complicated linear algebra question". But I agree, a lot of MLE roles don't get asked such things. I think the OP's guide is closer to interviews I've seen for phd programs.
    • whimsicalism 5y ago
      > Hopefully you aren't equating "eigenvectors" to "complicated linear algebra question". They explicitly say something harder than eigenvectors in the GP. I was imagining something involving the spectral theorem or something like that, ie. beyond the most basic linear algebra. OPs guide seems to cover plenty of things I'd expect someone to learn in undergrad, I think I touched on almost all of this - except for stuff involving jax and recent CNN architectures, both of which can easily be supplemented online.
• uoaei 5y ago
  The difference between trivia and meaty knowledge is somewhat contextually dependent, but an understanding of how core probability and statistics concepts are integrated into the framework of machine learning by means of linear algebra and the other analytical tools is pretty damn useful to have substantive conversations about ML design decisions. Helps when everyone in the team speaks that language to keep up the momentum.