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What you need to know depends largely what you are working on - and that also holds for a successful Ph.D. candidate. Of course, it is good advice to be open-mi
by jll29 4y ago
What you need to know depends largely what you are working on - and that also holds for a successful Ph.D. candidate. Of course, it is good advice to be open-minded beyond one's narrow field of inquiry, as the OP suggests, but a long list of maths topics may not be helpful a lot to beginners.
The Ph.D. period is the time when you have some time available to acquire additional skills, and my advice is: try to strengthen those aspects of your education where you currently have the most glaring deficits. For instance,
take a statistics course if that is your weak spot, or learn a foreign language
if you have evaded that topic so far in your educational journey.
Read the main textbooks of your field and read and re-read ALL relevant papers for your actual Ph.D. topic, once you've been able to identify it (which may well take you most or all of your first year). Take details notes because nobody can remember most of that much highly concentrated advanced material. Try to find gaps: ask questions and find out if people have tried to answer them yet or not.
Interact with others e.g. at conferences, after meeting some people there by attending and networking in a prior year.
- SQueeeeeL 4y ago> Read the main textbooks of your field and read and re-read ALL relevant papers for your actual Ph.D. topic, once you've been able to identify I'm gonna be real as someone in grad school. Basically no PhD, grad students, or professors I know read full text books. I hear these ideas a lot, and they often sound like one of those Instagram influencer diets, that's completely unreasonable if you have any constraints in your life, and it's mostly been used to gate keep "real" scientists. Be well studied and knowledgeable about your problem domain, but you have a finite lifespan, so never feel bad that you aren't "educated enough".
- patrickkidger 4y agoAs the author of this article... I have read maybe one textbook cover-to-cover in my life. :D (Hands-on machine learning, by Geron, back when I made the jump math->ML.)
- SQueeeeeL 4y agoElements of Statistical Learning is my cover to cover read :), I just think it isn't a requirement to be a "good student"
- mkl 4y agoI have an applied maths PhD but no machine learning. Would you still recommend Geron for that purpose? BTW, I spotted a typo in the first paragraph of your thesis abstract: "neural networks and differential equation are two sides".
- patrickkidger 4y agoAha, I have probably read that sentence literally hundreds of times, and never spotted the typo. I will never be able to unsee that. Geron is good but now a bit out-of-date. No transformers (just CNNs/RNNs/etc.) and the coding component is all in scikit-learn and TensorFlow (rather than PyTorch or JAX). FWIW I did ask this question recently over at https://twitter.com/PatrickKidger/status/1602776438159339521 https://twitter.com/PatrickKidger/status/1602776438159339521, in case any of the responses are helpful.