6 ms·

Maybe I should start a PhD in ML as a university drop out, it sounds like a list of basic stuff taught within the first two years of any math/CS program, but I

Maybe I should start a PhD in ML as a university drop out, it sounds like a list of basic stuff taught within the first two years of any math/CS program, but I might miss something as it's far from being detailed...

thwayunion 4y ago
Now is a terrible time to start a PhD in ML. If you do a CS PhD, pick literally any other subfield.
- eddsh1994 4y ago
  Why?
  - sillysaurusx 4y ago
    It's trendy, and in general it's best to avoid trendy fields. But if you actually care about ML -- I think it's one of the best things in the world -- go for it! ML has never been more accessible.
    - l33t233372 4y ago
      Trendy fields often have lots of money in them in both the short term(grants, associateships, funding), and the medium term(first job prospects) as well as benefits like preferential appearance in journals(if two equally insightful articles are up for one spot, would you rather take the article on the topic many are interested in or the article from a much more niche area?) and possibly more low-hanging fruit since there is so much more work to piggyback on/respond to/extend.
  - thwayunion 4y ago
    The subfield is oversupplied with labor relative to the supply of good ideas worth working on for six years and advising capacity.
  - dekhn 4y ago
    because the very best machine learning models are distilled from postdoc tears.
    - drdunce 4y ago
      I think every significant model has come from industry. us postdocs just tweak hyperparameters and publish answers to questions about them that no one is ever likely to ask It's like the "will it blend" series, but for machine learning
- Zetobal 4y ago
  Sounds a lot like my dad telling me to go to law school instead of getting into cs. He thought the ultimate goal of cs is to make everyone in the field obsolete... well maybe he was right in the end.
  - amelius 4y ago
    Lawyers too (maybe they are first)
- verdverm 4y ago
  I'd argue that cross field is a good avenue for a PhD in CS. Work in collaboration with one of the sciences. There are plenty of papers to be had in application. Not everything in ML research needs to be algorithms. You'll likely find places where the models break down and need algo work too
- ad404b8a372f2b9 4y ago
  People have been saying this for as long as I've been hanging on ML forums, meanwhile the professors I work with can't keep up with industry demand for ML and ML adjacent roles and have to open up new classes.
LeanderK 4y ago
I think it's hard to get those basics in an undergrad program. CS often has the problem that the math basics are not teached as rigorously as it should (I think calculus I/II and linear algebra I/II should be exactly the same as the lectures for math). Then it misses measure theory (usually in calc III) and therefore you are going to miss solid fundamentals in probability. Optimization is also usually absent, it's sometimes squeezed into the numerical math. lecture but it shouldn't since numerics (or better scientific computing) is so important. You also want some statistics course. Math usually lacks the whole machine learning canon, from SVM to NNs, from bayesian methods to statistical learning theory. I just see the same statistics lecture everywhere building upon probability and deriving good estimators and their properties and that's all. Also, you might miss CS basics like algorithms and struggle with the basics of scientific computing (programming in C, knowing all your matrix decompositions etc.). Also, no knowledge of git, how to navigate a server just with a shell etc. I think you will either have to learn the missing parts after your undergraduate, for example as your master, first year in your phd, or be really lucky! I think most miss a significant chunk of the topics after completing their undergraduate.

thwayunion 4y ago

Now is a terrible time to start a PhD in ML. If you do a CS PhD, pick literally any other subfield.

eddsh1994 4y ago
Why?
- sillysaurusx 4y ago
  It's trendy, and in general it's best to avoid trendy fields. But if you actually care about ML -- I think it's one of the best things in the world -- go for it! ML has never been more accessible.
  - l33t233372 4y ago
    Trendy fields often have lots of money in them in both the short term(grants, associateships, funding), and the medium term(first job prospects) as well as benefits like preferential appearance in journals(if two equally insightful articles are up for one spot, would you rather take the article on the topic many are interested in or the article from a much more niche area?) and possibly more low-hanging fruit since there is so much more work to piggyback on/respond to/extend.
- thwayunion 4y ago
  The subfield is oversupplied with labor relative to the supply of good ideas worth working on for six years and advising capacity.
- dekhn 4y ago
  because the very best machine learning models are distilled from postdoc tears.
  - drdunce 4y ago
    I think every significant model has come from industry. us postdocs just tweak hyperparameters and publish answers to questions about them that no one is ever likely to ask It's like the "will it blend" series, but for machine learning
Zetobal 4y ago
Sounds a lot like my dad telling me to go to law school instead of getting into cs. He thought the ultimate goal of cs is to make everyone in the field obsolete... well maybe he was right in the end.
- amelius 4y ago
  Lawyers too (maybe they are first)
verdverm 4y ago
I'd argue that cross field is a good avenue for a PhD in CS. Work in collaboration with one of the sciences. There are plenty of papers to be had in application. Not everything in ML research needs to be algorithms. You'll likely find places where the models break down and need algo work too
ad404b8a372f2b9 4y ago
People have been saying this for as long as I've been hanging on ML forums, meanwhile the professors I work with can't keep up with industry demand for ML and ML adjacent roles and have to open up new classes.

LeanderK 4y ago

I think it's hard to get those basics in an undergrad program. CS often has the problem that the math basics are not teached as rigorously as it should (I think calculus I/II and linear algebra I/II should be exactly the same as the lectures for math). Then it misses measure theory (usually in calc III) and therefore you are going to miss solid fundamentals in probability. Optimization is also usually absent, it's sometimes squeezed into the numerical math. lecture but it shouldn't since numerics (or better scientific computing) is so important. You also want some statistics course. Math usually lacks the whole machine learning canon, from SVM to NNs, from bayesian methods to statistical learning theory. I just see the same statistics lecture everywhere building upon probability and deriving good estimators and their properties and that's all. Also, you might miss CS basics like algorithms and struggle with the basics of scientific computing (programming in C, knowing all your matrix decompositions etc.). Also, no knowledge of git, how to navigate a server just with a shell etc. I think you will either have to learn the missing parts after your undergraduate, for example as your master, first year in your phd, or be really lucky! I think most miss a significant chunk of the topics after completing their undergraduate.