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Frankly, MOOC-type machine learning- being able to do plain vanilla logistic regression or black-box deep learning techniques is not enough to get a job. This k

by shas3 10y ago

Frankly, MOOC-type machine learning- being able to do plain vanilla logistic regression or black-box deep learning techniques is not enough to get a job. This knowledge and experience has to be paired with one or more strengths: excellence in programming, grad-level math/stat/numerical skills/theoretical machine learning, domain specific expertise or experience (e.g. vision, audio, natural language, networks, geophysics, biomed, finance, etc.), proven ability to learn and adapt very fast.

omginternets 10y ago
> vision Could you elaborate on which elements of vision science are relevant? Do you have any examples of what these people end up working on?
- shas3 10y ago
  I loosely used the term to refer to computer vision, which overlaps a bit with vision science (especially color, perception, etc.). But yeah, computer vision.
czep 10y ago
> plain vanilla logistic regression Eh? It's not as easy as it sounds. Search for "mlelr" for a rather detailed illustration of how to code LR in C with only standard libraries. Now that was some fun to put together!
- shas3 10y ago
  But that does mean that you have pretty good C programming skills! I think your comment reinforces my claim about pairing MOOC-depth ML with other skills. (Edit: I'm not saying MOOC-depth derisively. Serious learners and autodidacts can go pretty deep with just MOOCs for guidance. I just mean prima facie, based on content of some of the lighter and more popular MOOCs like Andrew Ng's. Abu-Mostafa's on EdX is meatier.)
  - czep 10y ago
    I definitely agree: the overuse of techniques like logistic regression in introductory courses without sufficient mathematical background and context can lead to a "plug and chug" culture where the technique is applied without proper understanding of its assumptions and limitations. Anyone can be quickly taught how to run a logistic regression by calling one line of code from a high level library. But I'd argue it takes years of study to really know what you're doing.
ywfs3370 10y ago
So... for instance an MD with MOOC-type machine learning could make it in health related enterprises? Is that what you mean by domain specific expertise?
- shas3 10y ago
  That's stretching it. I think I should have qualified domain specific expertise as quantitative-domain specific expertise. Afaik, MD is more about human biology and meant to help you become a practicing doctor. PhD-biomedicine, MPH/MS-bioinformatics, or MD-PhD programs tend to have more substantial quantitative components than just an MD. Caveat: these are gross generalizations. I know some MDs who are competent enough in quantitative analysis to give any engineer a run for their money! Would an English MA or PhD, for example couple well with an ML-MOOC for natural language processing jobs? Possibly, but would involve a lot of effort to build the bridge. However, if someone has a mechanical engineering degree or a PhD in something similar, a MOOC-ML can work well to position them for a bunch of jobs in large mechanical engineering companies looking to jump on the ML bandwagon or ones traditionally strong in ML-type mechanical engineering like UTC.

by shas3 10y ago

omginternets 10y ago

> vision Could you elaborate on which elements of vision science are relevant? Do you have any examples of what these people end up working on?

shas3 10y ago
I loosely used the term to refer to computer vision, which overlaps a bit with vision science (especially color, perception, etc.). But yeah, computer vision.

czep 10y ago

> plain vanilla logistic regression Eh? It's not as easy as it sounds. Search for "mlelr" for a rather detailed illustration of how to code LR in C with only standard libraries. Now that was some fun to put together!

shas3 10y ago
But that does mean that you have pretty good C programming skills! I think your comment reinforces my claim about pairing MOOC-depth ML with other skills. (Edit: I'm not saying MOOC-depth derisively. Serious learners and autodidacts can go pretty deep with just MOOCs for guidance. I just mean prima facie, based on content of some of the lighter and more popular MOOCs like Andrew Ng's. Abu-Mostafa's on EdX is meatier.)
- czep 10y ago
  I definitely agree: the overuse of techniques like logistic regression in introductory courses without sufficient mathematical background and context can lead to a "plug and chug" culture where the technique is applied without proper understanding of its assumptions and limitations. Anyone can be quickly taught how to run a logistic regression by calling one line of code from a high level library. But I'd argue it takes years of study to really know what you're doing.

ywfs3370 10y ago

So... for instance an MD with MOOC-type machine learning could make it in health related enterprises? Is that what you mean by domain specific expertise?

shas3 10y ago
That's stretching it. I think I should have qualified domain specific expertise as quantitative-domain specific expertise. Afaik, MD is more about human biology and meant to help you become a practicing doctor. PhD-biomedicine, MPH/MS-bioinformatics, or MD-PhD programs tend to have more substantial quantitative components than just an MD. Caveat: these are gross generalizations. I know some MDs who are competent enough in quantitative analysis to give any engineer a run for their money! Would an English MA or PhD, for example couple well with an ML-MOOC for natural language processing jobs? Possibly, but would involve a lot of effort to build the bridge. However, if someone has a mechanical engineering degree or a PhD in something similar, a MOOC-ML can work well to position them for a bunch of jobs in large mechanical engineering companies looking to jump on the ML bandwagon or ones traditionally strong in ML-type mechanical engineering like UTC.