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A lot of practical difficulties with your proposal. A CS/AI PhD program is nothing like a Math/AMath/Stat PhD. In the latter, there is zero expectation that yo

by dxbydt 9y ago

A lot of practical difficulties with your proposal. A CS/AI PhD program is nothing like a Math/AMath/Stat PhD. In the latter, there is zero expectation that you will start producing anything in the first few semesters. In fact, it is explicitly required of you to load up on rigorous core courses, so that you can pass your prelims at the end of 2nd year and become a formal “PhD candidate”. The attrition rate in those programs is about 40% or more, so a lot of these people simply find out they don’t have the math maturity, drop out with a Masters at the end of2 years, get a job and call the whole thing off. So in the latter case, yes, such a person can follow your guidelines. In fact, most of the material you listed in called AMC ( applied math core ) or CACM or other abbrev...and is already taught as part of core. Now in the former case is where this particular student is. CS PhDs programs are a sort of weird beast in the US. They are housed in eng, not liberal arts. The expectations are to produce papers right out of the gate. Atleast lightweight papers, posters, something...you cannot coast for 2 years saying you are learning convex math & stoc calc. So if you read the material that comes out of students in that phase, it tends to be of low quality and heavy on empirical evidence ( i tested 7 functions on 3 datasets and these 2 came first, here are the charts and graphs). As the student matures, his papers gather more heft and by the time he graduates the final 2-3 papers will be very good....atleast that’s the expectation. Reality again is quite bleak and results are all over the place. Because of the hectic hiring climate, lots of cs grads will just take an ABD and get the hell out. 150k starting plus rsu is nothing to sneeze at. The ones who do finish tend to take a full 4+ years and are in the teens % of incoming cohort :( Also, you are not really permitted to sign up for whatever you want just because you have math deficiencies. Your advisor will have to sign off on each sem load. He has to ensure you are on track, not just following your own whimsical path into theoretical math because you fancy it. CS core is quite distinct from AMath and touches upon the material you mentioned very superficially. All in all, this student is between a rock and a hard place. Its not going to be easy for him at all if he truly wants to understand everything. Best bet is to do what the top reddit advice is - pick some narrow corner where you are comfy, write 2-3 papers in that corner, get the hell out and learn the rest later on your own time in your research career.

stochastic_monk 9y ago
I can only speak from experience in my CS PhD program, but my above recommendations are based directly on that experience. We have an exam after the first two years and a similar process. It depends on what your advisor expects/wants. Mine has been flexible. And I focused more on efficient software engineering and applications for prior methods than on new research as I got up to speed on other matters. It made obvious a lot of ways I could improve them, just by being forced to look at and implement all of the details under the hood. And, at least in my CS department, there is a very heavy emphasis on mathematics with the ML/AI folks. They coauthor a number of papers with the applied math department and the rest of their papers are mostly proofs. They'll usually back it up with proof-of-concept implementations, but in that regard, they're very much like researchers in applied math except that they use Python instead of MATLAB.

by dxbydt 9y ago

stochastic_monk 9y ago

I can only speak from experience in my CS PhD program, but my above recommendations are based directly on that experience. We have an exam after the first two years and a similar process. It depends on what your advisor expects/wants. Mine has been flexible. And I focused more on efficient software engineering and applications for prior methods than on new research as I got up to speed on other matters. It made obvious a lot of ways I could improve them, just by being forced to look at and implement all of the details under the hood. And, at least in my CS department, there is a very heavy emphasis on mathematics with the ML/AI folks. They coauthor a number of papers with the applied math department and the rest of their papers are mostly proofs. They'll usually back it up with proof-of-concept implementations, but in that regard, they're very much like researchers in applied math except that they use Python instead of MATLAB.