5 ms·

Here is a math major's perspective (I also have a C.S. master's from Stanford, a decent school for computer science). The real reason statistics is losing to m

by kiyoto 12y ago

Here is a math major's perspective (I also have a C.S. master's from Stanford, a decent school for computer science). The real reason statistics is losing to machine learning is M&M: money and marketing. Money is a huge factor when kids choose majors in college. Many of them have student loans to pay off, and for many of them, getting a high-paying job post college is a serious consideration. In this regard, statistics is a great major, but computer science is flat-out ridiculous. When big tech companies are offering a Stanford graduate with only a few summers of programming internships under the belt for 150k/year, naturally a lot of kids are lured into computer science. Then, once they start studying computer science, they discover this thing called machine learning. While I do think there is a difference in emphasis between stats and machine learning, the fundamentals are same, except the nomenclature sounds much cooler in machine learning. Nonparametric inference sounds esoteric and cryptic, but unsupervised learning sounds futuristic and cool. The biggest problem with both statistics and machine learning education, I would say, is their lack of emphasis on mathematical foundations. When I was in college, CS229 (Introduction to Machine Learning) was touted to be the hardest class at Stanford. Having helped my friends wade through CS229 problems (a lot of which comes down to wading through linear algebra and multi-variable differential calculus), I do not think this is remotely true: CS229 is hard because it attracts students who do not have the requisite mathematical maturity to learn statistics/machine learning in a serious way (I am not even talking about the real-analytic foundation of probability and calculus but rudimentary linear algebra and chain rules). Also, I do think CS229@Stanford is a great class that brings theory and practice together =) As for R/Python/Matlab/etc., I will let the zealots argue what's best. To me, they are like statistics and machine learning: similar with different emphasis.

wwweston 12y ago
> The biggest problem with both statistics and machine learning education, I would say, is their lack of emphasis on mathematical foundations. Any recommendations for someone who'd like to pick up sound (if modest) foundations?
- GFK_of_xmaspast 12y ago
  Hogg & Craig used to be one of the standard texts.
- jamii 12y ago
  I am a big fan of http://www.amazon.com/Probability-Computing-Randomized-Algorithms-Probabilistic/dp/0521835402/ref=sr_1_1?ie=UTF8&qid=1409098878&sr=8-1&keywords=probability+and+computing http://www.amazon.com/Probability-Computing-Randomized-Algor... . Every time it teaches a new chunk of theory it motivates and grounds it in some algorithmic application.
- sampo 12y ago
  A college-level textbook in linear algebra, another in calculus (must include vector calculus), and one in probability? For linear algebra and calculus, at least, Coursera also offers courses.
- kiyoto 12y ago
  Yes. For example, to follow CS229's lecture notes, the following material should suffice. - Linear Algebra Done Right by Sheldon Axler - Linear Algebra by Steven Levandosky - Linear Algebra and Multivariable Calculus, and Manifolds by Ted Shifrin (you probably do not need the manifolds part) Also, as paradoxical as it may sound, CS229 lecture notes themselves. The key here is to verify the details painstakingly. In math, we joke that whenever a book says something is "trivial" or "easy to verify" or "clear to show", it is a substantial exercise in disguise.
  - skybrian 12y ago
    Yes, the whole "trivial exercise" thing is in itself a big problem with how mathematics is taught. Someone who's struggling just to understand the text could probably really use a worked-out example.
    - dceddia 12y ago
      "Left as an exercise to the reader," the bane of my existence as a college student. I especially remember the Sipser book doing this. He'd show a super-simple example, like proving 2+2=4, and then the next thing would be actually interesting and would be left as an exercise. Awesome.
1971genocide 12y ago
I agree with your points so much. I am a applied Maths student (currently) and when looking for internships I noticed 80% of them are for CS related stuff. Imagine being me ( 19 years old ), offered a 15K/year job in my first year in university. My best job up until then was cleaning the floor at a mall. I have studied insane amount of mathematics all my life ( proofs after proofs ), how much longer do I need to wait until I am able to something of value in society ? Compared that to the amazing 14 weeks internship I did at a startup churning out frontend javascript. Those 14 weeks were the best weeks of my life, I never felt more confident and of value. My depression vanished overnight, I found a new aim in life, I treated coding the same way nations treat nuclear weapons. I learnt more things of value in those 14 weeks then I did in the previous 19 years, I didn't need to set up an alarm clock to wake up in the morning. There was no hand holding, because I was getting paid. I spent the rest of my waking hour churing code with no need for external motivation. I got really angry at society for misleading me for such a long time because of how easy it was for me to do something useful, finally. I see my friends struggling with the same problems and feel so much of young ppl's time is wasted, In my mind its a crime. I think as always mathematicians fail to grasp the human side of the argument, why are they complaining about lack of interest in statistics ? its their own fault this problem exists. There is no Neil Degrasse Tyson for statistics, there is no mark zuckerberg for stats, you don't need to hire a PR department. Just show us the wonderful magic that you speak of and we are smart enough to figure out why we should dedicate our lives to your cause. This is why if some statistical axiom/theory doesn't have a CS applied part I became really skeptical of doing that course.
- eli_gottlieb 12y ago
  >I think as always mathematicians fail to grasp the human side of the argument, why are they complaining about lack of interest in statistics ? its their own fault this problem exists. There is no Neil Degrasse Tyson for statistics, there is no mark zuckerberg for stats, you don't need to hire a PR department. Just show us the wonderful magic that you speak of and we are smart enough to figure out why we should dedicate our lives to your cause. It doesn't help that textbooks and course materials in mathematics sometimes appear to be designed for working mathematicians more than for students, with a lot of outright hostility towards the very concept of students learning maths. (I acknowledge that this comes from Math professors hating the time they spend teaching STEM weed-out courses, but I still think that overall, Mathematics is much worse than even theoretical computer science at avoiding things like Notation Proliferation Syndrome and Greek Letter Syndrome that make it artificially hard to learn. Explain the rigor, yes, but also explain the meaning and the concepts such that a decent learner should understand them well enough to reconstruct the rigorous parts on-the-fly as he or she needs.)
phaus 12y ago
>When big tech companies are offering a Stanford graduate with only a few summers of programming internships under the belt for 150k/year Aren't most of these offers in a part of CA where the cost of living is just as ridiculous as the salary? Therefore making the salary not ridiculous at all. I work in the DC area, and veteran programmers normally can't get anywhere close to that unless its a contract job with no benefits and shitty working conditions. Even still, it will likely be about 30k less than that.
- argonaut 12y ago
  All-in compensation for a top student out of a top college (think Stanford, MIT, Berkeley) in SV can be $200k. But keep in mind that the high cost of living really only applies to certain areas (albeit desirable ones like SF, Palo Alto, even Mountain View). There are tons of affordable locations if you are willing to have a 30 minute commute and are willing to accept sketchier environments (Berkeley, Oakland, other East Bay locations, East Palo Alto, etc.).
  - eli_gottlieb 12y ago
    Berkeley is sketchy now?
    - argonaut 12y ago
      The cheaper parts of Berkeley are sketchy, yes.
  - jghn 12y ago
    "top student out of a top college" doesn't exactly describe a lot of individuals (10? 15?), which makes that compensation value an extreme outlier.
    - argonaut 12y ago
      Allow me to revise that to "top student at a decent school." (School is actually less important). I would say it describes >200 people every year (take the top 5-10% of Stanford, Harvard, Berkeley, MIT, CMU, Princeton, UPenn, UT Austin, Caltech, a couple others, and then maybe the top 2-5% at UCLA, UCSD, CU-Boulder, dozens more schools.
east2west 12y ago
I suspect money is a big motivation behind all the hand-wringing by statisticians. It is about jobs, grants, prestige. I don't blame them; people want these things. I defintively see a competition between computer scientists and statisticians for the mantle of date scientists. When the current bubble bursts and the hype dies down, one side could have won and the world could be very different, or not. What do I know? I am a little torn between realistic business demand of paying least money to get jobs done and thorough mastery and understanding of theories. Look at biostatistics; there master graduates become data analysts and process data. They know the nomenclature and can understand instructions from more advanced statisticians. The majority of practitioners don't know proofs and they carry off their tasks reasonably well. I would call all but very select few statisticians applied statitisticans and the world is fine with that. I am not sure machine learning industry will not turn out like this, although I am not looking forward to that day. I would just note that numerical optimization is a big deal in machine learning while I know of no statistical department strong in it. The popularity of SVM owes a load of gratitude to pracitical breakthrough in dynamic optimization. But if one elevates to the level of functional approximation then statistics is similar to almost all engineering fields. In fact, I suspect statisticians have more common with engineers than scientists. I bet Eric Cantor didn't care that polling results had margins of errors but that he lost his House seat due in part to overconfidence coming from unreliable statistics. Real world applicability, not theoretical nicety.
- sesqu 12y ago
  > I am not sure machine learning industry will not turn out like this I am certain it will. The machine learning field is already rife with best practices, general-use algorithms, software solutions and tutorials.
eli_gottlieb 12y ago
>As for R/Python/Matlab/etc., I will let the zealots argue what's best. To me, they are like statistics and machine learning: similar with different emphasis. Having tried working with Matlab and having extensive experience with Python, my verdict is: Haskell or Ocaml. Ok, but seriously, once I understand just fine what algorithm I'm implementing, doing numerical computation in these all-dynamic languages is atrocious. I could have saved hours of runtime if I'd been allowed and able to just write the damned algorithms in a statically-typed language that compiles to machine code, on top of saving myself debugging time.

by kiyoto 12y ago

wwweston 12y ago

> The biggest problem with both statistics and machine learning education, I would say, is their lack of emphasis on mathematical foundations. Any recommendations for someone who'd like to pick up sound (if modest) foundations?

GFK_of_xmaspast 12y ago
Hogg & Craig used to be one of the standard texts.
jamii 12y ago
I am a big fan of http://www.amazon.com/Probability-Computing-Randomized-Algorithms-Probabilistic/dp/0521835402/ref=sr_1_1?ie=UTF8&qid=1409098878&sr=8-1&keywords=probability+and+computing http://www.amazon.com/Probability-Computing-Randomized-Algor... . Every time it teaches a new chunk of theory it motivates and grounds it in some algorithmic application.
sampo 12y ago
A college-level textbook in linear algebra, another in calculus (must include vector calculus), and one in probability? For linear algebra and calculus, at least, Coursera also offers courses.
kiyoto 12y ago
Yes. For example, to follow CS229's lecture notes, the following material should suffice. - Linear Algebra Done Right by Sheldon Axler - Linear Algebra by Steven Levandosky - Linear Algebra and Multivariable Calculus, and Manifolds by Ted Shifrin (you probably do not need the manifolds part) Also, as paradoxical as it may sound, CS229 lecture notes themselves. The key here is to verify the details painstakingly. In math, we joke that whenever a book says something is "trivial" or "easy to verify" or "clear to show", it is a substantial exercise in disguise.
- skybrian 12y ago
  Yes, the whole "trivial exercise" thing is in itself a big problem with how mathematics is taught. Someone who's struggling just to understand the text could probably really use a worked-out example.
  - dceddia 12y ago
    "Left as an exercise to the reader," the bane of my existence as a college student. I especially remember the Sipser book doing this. He'd show a super-simple example, like proving 2+2=4, and then the next thing would be actually interesting and would be left as an exercise. Awesome.

1971genocide 12y ago

I agree with your points so much. I am a applied Maths student (currently) and when looking for internships I noticed 80% of them are for CS related stuff. Imagine being me ( 19 years old ), offered a 15K/year job in my first year in university. My best job up until then was cleaning the floor at a mall. I have studied insane amount of mathematics all my life ( proofs after proofs ), how much longer do I need to wait until I am able to something of value in society ? Compared that to the amazing 14 weeks internship I did at a startup churning out frontend javascript. Those 14 weeks were the best weeks of my life, I never felt more confident and of value. My depression vanished overnight, I found a new aim in life, I treated coding the same way nations treat nuclear weapons. I learnt more things of value in those 14 weeks then I did in the previous 19 years, I didn't need to set up an alarm clock to wake up in the morning. There was no hand holding, because I was getting paid. I spent the rest of my waking hour churing code with no need for external motivation. I got really angry at society for misleading me for such a long time because of how easy it was for me to do something useful, finally. I see my friends struggling with the same problems and feel so much of young ppl's time is wasted, In my mind its a crime. I think as always mathematicians fail to grasp the human side of the argument, why are they complaining about lack of interest in statistics ? its their own fault this problem exists. There is no Neil Degrasse Tyson for statistics, there is no mark zuckerberg for stats, you don't need to hire a PR department. Just show us the wonderful magic that you speak of and we are smart enough to figure out why we should dedicate our lives to your cause. This is why if some statistical axiom/theory doesn't have a CS applied part I became really skeptical of doing that course.

eli_gottlieb 12y ago
>I think as always mathematicians fail to grasp the human side of the argument, why are they complaining about lack of interest in statistics ? its their own fault this problem exists. There is no Neil Degrasse Tyson for statistics, there is no mark zuckerberg for stats, you don't need to hire a PR department. Just show us the wonderful magic that you speak of and we are smart enough to figure out why we should dedicate our lives to your cause. It doesn't help that textbooks and course materials in mathematics sometimes appear to be designed for working mathematicians more than for students, with a lot of outright hostility towards the very concept of students learning maths. (I acknowledge that this comes from Math professors hating the time they spend teaching STEM weed-out courses, but I still think that overall, Mathematics is much worse than even theoretical computer science at avoiding things like Notation Proliferation Syndrome and Greek Letter Syndrome that make it artificially hard to learn. Explain the rigor, yes, but also explain the meaning and the concepts such that a decent learner should understand them well enough to reconstruct the rigorous parts on-the-fly as he or she needs.)

phaus 12y ago

>When big tech companies are offering a Stanford graduate with only a few summers of programming internships under the belt for 150k/year Aren't most of these offers in a part of CA where the cost of living is just as ridiculous as the salary? Therefore making the salary not ridiculous at all. I work in the DC area, and veteran programmers normally can't get anywhere close to that unless its a contract job with no benefits and shitty working conditions. Even still, it will likely be about 30k less than that.

argonaut 12y ago
All-in compensation for a top student out of a top college (think Stanford, MIT, Berkeley) in SV can be $200k. But keep in mind that the high cost of living really only applies to certain areas (albeit desirable ones like SF, Palo Alto, even Mountain View). There are tons of affordable locations if you are willing to have a 30 minute commute and are willing to accept sketchier environments (Berkeley, Oakland, other East Bay locations, East Palo Alto, etc.).
- eli_gottlieb 12y ago
  Berkeley is sketchy now?
  - argonaut 12y ago
    The cheaper parts of Berkeley are sketchy, yes.
- jghn 12y ago
  "top student out of a top college" doesn't exactly describe a lot of individuals (10? 15?), which makes that compensation value an extreme outlier.
  - argonaut 12y ago
    Allow me to revise that to "top student at a decent school." (School is actually less important). I would say it describes >200 people every year (take the top 5-10% of Stanford, Harvard, Berkeley, MIT, CMU, Princeton, UPenn, UT Austin, Caltech, a couple others, and then maybe the top 2-5% at UCLA, UCSD, CU-Boulder, dozens more schools.

east2west 12y ago

I suspect money is a big motivation behind all the hand-wringing by statisticians. It is about jobs, grants, prestige. I don't blame them; people want these things. I defintively see a competition between computer scientists and statisticians for the mantle of date scientists. When the current bubble bursts and the hype dies down, one side could have won and the world could be very different, or not. What do I know? I am a little torn between realistic business demand of paying least money to get jobs done and thorough mastery and understanding of theories. Look at biostatistics; there master graduates become data analysts and process data. They know the nomenclature and can understand instructions from more advanced statisticians. The majority of practitioners don't know proofs and they carry off their tasks reasonably well. I would call all but very select few statisticians applied statitisticans and the world is fine with that. I am not sure machine learning industry will not turn out like this, although I am not looking forward to that day. I would just note that numerical optimization is a big deal in machine learning while I know of no statistical department strong in it. The popularity of SVM owes a load of gratitude to pracitical breakthrough in dynamic optimization. But if one elevates to the level of functional approximation then statistics is similar to almost all engineering fields. In fact, I suspect statisticians have more common with engineers than scientists. I bet Eric Cantor didn't care that polling results had margins of errors but that he lost his House seat due in part to overconfidence coming from unreliable statistics. Real world applicability, not theoretical nicety.

sesqu 12y ago
> I am not sure machine learning industry will not turn out like this I am certain it will. The machine learning field is already rife with best practices, general-use algorithms, software solutions and tutorials.

eli_gottlieb 12y ago

>As for R/Python/Matlab/etc., I will let the zealots argue what's best. To me, they are like statistics and machine learning: similar with different emphasis. Having tried working with Matlab and having extensive experience with Python, my verdict is: Haskell or Ocaml. Ok, but seriously, once I understand just fine what algorithm I'm implementing, doing numerical computation in these all-dynamic languages is atrocious. I could have saved hours of runtime if I'd been allowed and able to just write the damned algorithms in a statically-typed language that compiles to machine code, on top of saving myself debugging time.