How to Machine Learn • Hacker News Board

Re: How to Machine Learn

#11

Post by gwulf » Mon, Feb 16, 2015, 3:22 AM UTC

anindyabd wrote: post #5

The website for the project linked to on the first line is based on this Bootstrap template: http://startbootstrap.com/template-overviews/grayscale/ . At least they could have changed the background photo!

Good find! The goal was to wrap the feature engineer scripts up with a front end, so design was an after thought.

Re: How to Machine Learn

#12

Post by hiddencost » Mon, Feb 16, 2015, 3:23 AM UTC

fa wrote: post #6

hiddencost wrote: post #3

Earlier quoted context omitted.

http://math.stackexchange.com/questions/344879/how-does-line... Pretty much all of machine learning relies on linear algebra. Most scientific computing relies on lI near algebra. Graph algorithms often use linear algebra. If you just want to be a Web Dev or an app developer, it probably doesn't matter, tho. But if you just want to do Web Dev or mobile apps then you don't really need most of computer science.

Well, if I remember my university's beginner linear algebra course, there were many topics on the syllabus only due to historic accident, ancestor worship, and theoretical necessities: I remember parallelepipeds, Cramer's rule, solving eigensystems by solving for a polynomial's zeros... Let me tell you how many times I've used parallelepipeds, Cramer's rule, or found eigenvalues via the quadratic formula in the 12 ye…

Sounds like your college sucked, but that doesn't make it a fair generalization.

Re: How to Machine Learn

#13

Post by gms7777 » Mon, Feb 16, 2015, 3:26 AM UTC

mattbettinson wrote: post #2

I'm a freshman in university right now, how is linear algebra helpful in computer science? I'm finding it hard to stay motivated as I can't think of any uses outside of graphics. Maybe I'm just not far along in the course though.

Linear algebra comes up all the time in advanced courses. Certainly in graphics, but also if you do any machine learning, optimization, probabilistic algorithms, any sort of scientific computation. I never took a linear algebra course, convincing myself that I could just pick it up as I went along and getting into grad level computer science courses was ultimately a rather painful experience because of this. If I were you, I'd try to take it seriously and really try to develop an intuitive feel for linear algebra because (depending on what courses you want to take), it can really save you time and headaches in the future.

Re: How to Machine Learn

#14

Post by hiddencost » Mon, Feb 16, 2015, 3:28 AM UTC

karmacondon wrote: post #9

fa wrote: post #6

Earlier quoted context omitted.

Well, if I remember my university's beginner linear algebra course, there were many topics on the syllabus only due to historic accident, ancestor worship, and theoretical necessities: I remember parallelepipeds, Cramer's rule, solving eigensystems by solving for a polynomial's zeros... Let me tell you how many times I've used parallelepipeds, Cramer's rule, or found eigenvalues via the quadratic formula in the 12 ye…

Seconded. Same for discrete mathematics and differential equations. Interesting to learn about, but pretty much worthless as soon as you set foot off of campus. I'd love to see comments from anyone who has practically used any of the information from those classes as a part of their daily duties as a programmer of any kind.

Sure. I work in ML, in industry. Singular value decomposition and related methods are huge. Understanding basis vectors. Most of the notation I use every day. The intuitive understanding of linear algebra and ability to read papers that rely on it. a lot of ML relies on understanding data as points in high dimensional space.

a lot of the stuff you're mentioning is required for what I'd consider the really interesting topics in CS, stuff like ML, operations research, scientific computing.

Re: How to Machine Learn

#15

Post by j2kun » Mon, Feb 16, 2015, 4:03 AM UTC

fa wrote: post #6

hiddencost wrote: post #3

Earlier quoted context omitted.

http://math.stackexchange.com/questions/344879/how-does-line... Pretty much all of machine learning relies on linear algebra. Most scientific computing relies on lI near algebra. Graph algorithms often use linear algebra. If you just want to be a Web Dev or an app developer, it probably doesn't matter, tho. But if you just want to do Web Dev or mobile apps then you don't really need most of computer science.

Well, if I remember my university's beginner linear algebra course, there were many topics on the syllabus only due to historic accident, ancestor worship, and theoretical necessities: I remember parallelepipeds, Cramer's rule, solving eigensystems by solving for a polynomial's zeros... Let me tell you how many times I've used parallelepipeds, Cramer's rule, or found eigenvalues via the quadratic formula in the 12 ye…

I can point to people who used eigenvalues to make billions and change the world. So... maybe they're not useless.

Re: How to Machine Learn

#16

Post by dchapp » Mon, Feb 16, 2015, 4:08 AM UTC

j2kun wrote: post #15

fa wrote: post #6

Earlier quoted context omitted.

Well, if I remember my university's beginner linear algebra course, there were many topics on the syllabus only due to historic accident, ancestor worship, and theoretical necessities: I remember parallelepipeds, Cramer's rule, solving eigensystems by solving for a polynomial's zeros... Let me tell you how many times I've used parallelepipeds, Cramer's rule, or found eigenvalues via the quadratic formula in the 12 ye…

I can point to people who used eigenvalues to make billions and change the world. So... maybe they're not useless.

It's not that eigenvalues per se are useless--they're plainly not--but that no one finds eigenvalues in practice by computing the characteristic polynomial and solving for its roots. Unfortunately, that computation is often found in HW and exams in US undergraduate linear algebra courses.

Re: How to Machine Learn

#17

Post by powerset » Mon, Feb 16, 2015, 4:11 AM UTC

mattbettinson wrote: post #2

I'm a freshman in university right now, how is linear algebra helpful in computer science? I'm finding it hard to stay motivated as I can't think of any uses outside of graphics. Maybe I'm just not far along in the course though.

A nice little pep talk motivating study of linear algebra and why it's useful (e.g. pagerank):

http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr...

Re: How to Machine Learn

#18

Post by j2kun » Mon, Feb 16, 2015, 4:13 AM UTC

dchapp wrote: post #16

j2kun wrote: post #15

Earlier quoted context omitted.

I can point to people who used eigenvalues to make billions and change the world. So... maybe they're not useless.

It's not that eigenvalues per se are useless--they're plainly not--but that no one finds eigenvalues in practice by computing the characteristic polynomial and solving for its roots. Unfortunately, that computation is often found in HW and exams in US undergraduate linear algebra courses.

People also don't only solve for eigenvalues computationally. Knowing about all the perspectives of eigenvalues helps. I agree the computations are dumb, but if you propose an intro linear algebra class with no computations to soak up test scores you will get far more protests.

Re: How to Machine Learn

#19

Post by fa » Mon, Feb 16, 2015, 4:18 AM UTC

karmacondon wrote: post #9

fa wrote: post #6

Earlier quoted context omitted.

Well, if I remember my university's beginner linear algebra course, there were many topics on the syllabus only due to historic accident, ancestor worship, and theoretical necessities: I remember parallelepipeds, Cramer's rule, solving eigensystems by solving for a polynomial's zeros... Let me tell you how many times I've used parallelepipeds, Cramer's rule, or found eigenvalues via the quadratic formula in the 12 ye…

Seconded. Same for discrete mathematics and differential equations. Interesting to learn about, but pretty much worthless as soon as you set foot off of campus. I'd love to see comments from anyone who has practically used any of the information from those classes as a part of their daily duties as a programmer of any kind.

I watched Gilbert Strang's video lectures on linear algebra (the MIT freshman course) for preparation for my PhD qualifier exam, and as a third year grad student, I could appreciate the relevance of almost every single topic in the class. That is, seven years after freshman linear algebra and with countless applications programmed, papers read and implemented, and theoretical/applied classes taken, it "all made sense" (don't ask me what a freshman is supposed to make of that material, other than to acquire it at a very abstract superficial level).

The early classes are the prerequisites for every and anything you might wind up doing with math. Including becoming a math prof, or a web dev, or dropping out. Nobody tells you, for every section of every textbook you have to read, what its myriad applications might be, and you can't get a customized build of just the topics you want.

But we're all startup people here right? Can this shortcoming be fixed? Can we make a detailed dependency graph of topics in applied mathematics, which could potentially be used to generate custom learning builds?

Re: How to Machine Learn

#20

Post by fa » Mon, Feb 16, 2015, 4:22 AM UTC

dchapp wrote: post #16

j2kun wrote: post #15

Earlier quoted context omitted.

I can point to people who used eigenvalues to make billions and change the world. So... maybe they're not useless.

It's not that eigenvalues per se are useless--they're plainly not--but that no one finds eigenvalues in practice by computing the characteristic polynomial and solving for its roots. Unfortunately, that computation is often found in HW and exams in US undergraduate linear algebra courses.

This is especially tragic because matrix factorization algorithms are so deep and interesting, theory and programming-wise! LU, Cholesky, QR, eigendecomposition, SVD, mmmm. Round-off error tolerance, convergence criteria, stability, yum.

Characteristic qualities and root finding: bleh.