[D] Any other PhD students feel underprepared and that the bar is too low?

Reddit r/MachineLearning / 3/25/2026

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Key Points

  • A Reddit post asks whether other ML PhD students feel underprepared due to missing theoretical foundations, describing how the author now realizes they did not have enough theory to keep up.
  • The author notes that they are often forced to “scramble” to acquire theoretical knowledge after starting the program, and says this seems common in ML academia rather than just imposter syndrome.
  • They question a disconnect between how frequently theories like the universal approximation theorem are cited and how few students can actually follow the theorem’s proof.
  • The post invites discussion on what students should do to address this reality and close the gap between applied work and deeper theory comprehension.

Hello! I started my PhD a year and a half ago, and I feel like when I did everyone was kind of dismissive of how much/little theoretical knowledge I have or am missing.

Now that I’ve been here a year I can say with confidence that I didn’t have enough theory, and am constantly scrambling to acquire it.

This isn’t like an imposter syndrome rant, I think that this is quite common in ML academia, I just don’t know what to do with that reality, and wonder what folks on here think.

Like why is it that despite citing the universal approximation theorem, and spending all our time working on applying it, so few of us can actually follow its proof?

submitted by /u/Scrungo__Beepis
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