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[D] How do you keep the fundamentals fresh as a PhD student?

I feel like the knowledge that I can easily recall has become more and more narrow as the years (and my PhD) goes by, and while (I think) I have a very strong understanding of my sub-subfield, I still have to spend a decent amount of time refreshing on the fundamentals before internship interviews.

I’ve taken many (and TA’d several) courses in probability theory, statistical ML, algorithms, etc. but since none of the models in my field are really probabilistic and we don’t use most fundamental algorithms or data structures I’m finding it more difficult to recall those topics during interviews. Given enough time I can find a DP recurrence relation but not as fast as I would like since I haven’t had to do this in years. Ditto for questions like implementing EM from scratch.

How do you keep these topics fresh? Do you occasionally look over old notes, grind leetcode once or twice a week, etc?

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.