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Just know stuff (or, how to achieve success in a machine learning PhD)

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Re: Just know stuff (or, how to achieve success in a machine learning PhD)

#81

Post by patrickkidger »

SQueeeeeL wrote: post #52
jll29 wrote: post #12

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 strength…

> 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…

As 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.)

Re: Just know stuff (or, how to achieve success in a machine learning PhD)

#82

Post by l33t233372 »

sillysaurusx wrote: post #16
eddsh1994 wrote: post #11

Earlier quoted context omitted.

Why?

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.

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.

Re: Just know stuff (or, how to achieve success in a machine learning PhD)

#83

Post by l33t233372 »

burnished wrote: post #49
amelius wrote: post #46

Good references seem to be missing.

Agreed that it would be nice, but exactly how much of the work of becoming educated are you willing to ask this person to do on your behalf?

If they are as educated on these topics as they claim, adding references is a trivial matter and generally greatly improves the value of the work.

Re: Just know stuff (or, how to achieve success in a machine learning PhD)

#84

Post by mi_lk »

gautamcgoel wrote: post #74

I just wrapped up a machine learning PhD at Caltech (now doing a postdoc in ML at Berkeley) and I disagree strongly with this article. What matters isn't knowing a bunch of random stuff, but rather writing/speaking skills, a willingness to learn new things, perseverance in the face of setbacks, creativity, having enough EQ to navigate the advisor-advisee relationship and departmental politics, and most of all, an abi…

This. The article is meh in terms of advices, and even serves more as a self-promotion (which apparently he is good at and that's something to learn). Maybe it somehow works for him and it's fine, but it's no more useful than a table of contents.

Re: Just know stuff (or, how to achieve success in a machine learning PhD)

#85

Post by burnished »

l33t233372 wrote: post #83
burnished wrote: post #49

Earlier quoted context omitted.

Agreed that it would be nice, but exactly how much of the work of becoming educated are you willing to ask this person to do on your behalf?

If they are as educated on these topics as they claim, adding references is a trivial matter and generally greatly improves the value of the work.

I am going to disagree with you there. I have physical copies of a couple books I recommend and making them easily referenced can be a bit of a pain - not difficult but may be not worth the effort if I was compiling a list of topics to serve as my general answer to a question I receive frequently

Re: Just know stuff (or, how to achieve success in a machine learning PhD)

#86

Post by SQueeeeeL »

patrickkidger wrote: post #81
SQueeeeeL wrote: post #52

Earlier quoted context omitted.

> 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…

As 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.)

Elements of Statistical Learning is my cover to cover read :), I just think it isn't a requirement to be a "good student"

Re: Just know stuff (or, how to achieve success in a machine learning PhD)

#87

Post by patrickkidger »

gautamcgoel wrote: post #74

I just wrapped up a machine learning PhD at Caltech (now doing a postdoc in ML at Berkeley) and I disagree strongly with this article. What matters isn't knowing a bunch of random stuff, but rather writing/speaking skills, a willingness to learn new things, perseverance in the face of setbacks, creativity, having enough EQ to navigate the advisor-advisee relationship and departmental politics, and most of all, an abi…

I think those are all things you need for life.

But what you do need for specifically a PhD? I argue that "knowing stuff" is what is necessary -- and that indeed it's essentially the purpose of the whole academic institutiom.

Re: Just know stuff (or, how to achieve success in a machine learning PhD)

#88

Post by brilee »

As somebody from the U.S., I'm struck by how much people from the U.K. value a strong background in theory. At Google my experience has been "what matters is how smart you are, whether you understand the problem we're trying to solve, and whether you have creative solutions". When I applied for DeepMind some time ago, I was grilled, rapid-fire, a hundred questions covering the breadth of a rigorous undergraduate education in linear algebra, stats, ML, calculus, etc.. They seemed content to measure my intelligence by seeing how rapidly and deeply I had assimilated standard courses, rather than by seeing how I approached a problem I'd never seen before.

This guy is obviously talented, but also he comes from a tradition of optimizing for this kind of academic culture. You would be similarly weirded out by the leetcode fetish in tech if that's not what you were used to. I think that's what many commenters are missing.

Re: Just know stuff (or, how to achieve success in a machine learning PhD)

#89

Post by ModernMech »

patrickkidger wrote: post #87
gautamcgoel wrote: post #74

I just wrapped up a machine learning PhD at Caltech (now doing a postdoc in ML at Berkeley) and I disagree strongly with this article. What matters isn't knowing a bunch of random stuff, but rather writing/speaking skills, a willingness to learn new things, perseverance in the face of setbacks, creativity, having enough EQ to navigate the advisor-advisee relationship and departmental politics, and most of all, an abi…

I think those are all things you need for life . But what you do need for specifically a PhD ? I argue that "knowing stuff" is what is necessary -- and that indeed it's essentially the purpose of the whole academic institutiom.

A Ph.D. isn't about knowing things, it's about doing research. Recipients of the degree are given the title "doctor" not for what they know, but because they are first and foremost teachers of knowledge (from the Latin docere, meaning to teach).

"Knowing stuff" is enough to get you to the point where you can formulate a good research question, but being a good researcher is a much broader skillset than just knowing things. And consequently, just knowing things won't get you a Ph.D., because getting one requires you to talk about your research. A lot. Like, all the time. And after all, the last step in getting the Ph.D. is called a "defense", not an "examination", because you are not there to tell anyone what you know -- you are there to defend what you know, and that's a different skillset than gaining knowledge or even disseminating knowledge.

I guess all that is to say, you could know everything in the world, but if you lack the skills to tell anyone else about that knowledge, you'll never get a Ph.D.

Re: Just know stuff (or, how to achieve success in a machine learning PhD)

#90

Post by xmprt »

brilee wrote: post #88

As somebody from the U.S., I'm struck by how much people from the U.K. value a strong background in theory. At Google my experience has been "what matters is how smart you are, whether you understand the problem we're trying to solve, and whether you have creative solutions". When I applied for DeepMind some time ago, I was grilled, rapid-fire, a hundred questions covering the breadth of a rigorous undergraduate educ…

I've noticed that cultural difference too. I think there are things to take away from both approaches. Extremely knowledgeable people with a lot of background in theory should get better at understanding and creatively applying that knowledge to new problems. And people who are great at problem solving should learn more theory instead of expecting themselves to just materialize the best solution out of thin air.
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