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This is so false. Applying machine learning to a real world problem requires correct intuition and the ability to quantify tradeoffs mathematically. This is dev

This is so false. Applying machine learning to a real world problem requires correct intuition and the ability to quantify tradeoffs mathematically. This is developed by understanding the math behind the model and what the tradeoffs are.

splike 10y ago
I completely disagree. Yes, we need to quantify tradeoffs between models mathematically, but that does not not require knowledge of the mathematics behind the models themselves. With cross validation, I can estimate the effectiveness of many black box models, without looking inside them. This step is called error estimation, and comes before model selection. I can arrive at a pretty good model by a combination of correct methodology and brute force. It is this methodology that makes up much more of the overall picture. You could give me a black box, a rough range of parameters it takes, and I can tell you how likely it is to work well. This approach doesn't scale well to bigger problems, but I doubt tackling Big Data problems is the intention behind this course.
- bartkappenburg 10y ago
  And I disagree with that. Tuning parameters and selecting features needs (a) understanding of the model(s) used and (b) an understanding of the data. 'Brute forcing' these steps can grow exponentially in time (eg. feature selection out of n features takes 2^n combinations) and makes your approach not only very inefficient but also doesn't predict if you have a good model. Your approach makes sensitivity analysis makes very very hard.
  - pitchka 10y ago
    And yet again these stuff can be automated with contextual bandits or gaussian processes. Instead of bruteforcing one can just put yet another system to find the parameters for them.
- forgetsusername 10y ago
  >I completely disagree Don't worry, as with anything there's a certain subset of people who actually know the underlying principles behind a subject, and for some reason feel threatened when those principles are abstracted away, as if their knowledge is now wasted. But that's the natural progression of things. Sorry. It's funny it happens in a community of programmers though, where half of the tools that are used everyday are blackboxes that few really understand. Like the computer itself.
  - flipgimble 10y ago
    And its completely fine to be the developer who uses pre-made algorithmic block for their specific problem. However you will always be several years behind the current state of art. For example deep-learning really revolutionized the state of the art in image recognition in 2012 by winning academic competitions. It took about 3-5 years for those deep learning algorithms to get productized into packages like tensorflow, with high production tutorials and videos, so it was accessible to non-academics. I don't think people that know the underlying principles of machine learning are threatened (Thats sounds like pretty insecure world view on your part). They operate in a different context where you want to push the state of the art in machine learning algorithms, instead of just applying existing best-practices to your specific problem.
    - forgetsusername 10y ago
      >However you will always be several years behind the current state of art I agree with your post, but 99.9% of people who will be applying ML via black-box algorithm in the next decade won't be participating in, or at all concerned with, the state-of-the-art. In the same way that most of us aren't concerned about state-of-the-art chip design. I can do a regression analysis with a couple clicks in excel. I need little knowledge beyond how to interpret results. Sure, the underlying data might violate some assumptions, but it's rare (and there are tools for that). And let's face it, the most popular applications by amateurs will be marketing related, not cancer-curing related.
      - 10y ago

splike 10y ago

I completely disagree. Yes, we need to quantify tradeoffs between models mathematically, but that does not not require knowledge of the mathematics behind the models themselves. With cross validation, I can estimate the effectiveness of many black box models, without looking inside them. This step is called error estimation, and comes before model selection. I can arrive at a pretty good model by a combination of correct methodology and brute force. It is this methodology that makes up much more of the overall picture. You could give me a black box, a rough range of parameters it takes, and I can tell you how likely it is to work well. This approach doesn't scale well to bigger problems, but I doubt tackling Big Data problems is the intention behind this course.

bartkappenburg 10y ago
And I disagree with that. Tuning parameters and selecting features needs (a) understanding of the model(s) used and (b) an understanding of the data. 'Brute forcing' these steps can grow exponentially in time (eg. feature selection out of n features takes 2^n combinations) and makes your approach not only very inefficient but also doesn't predict if you have a good model. Your approach makes sensitivity analysis makes very very hard.
- pitchka 10y ago
  And yet again these stuff can be automated with contextual bandits or gaussian processes. Instead of bruteforcing one can just put yet another system to find the parameters for them.
forgetsusername 10y ago
>I completely disagree Don't worry, as with anything there's a certain subset of people who actually know the underlying principles behind a subject, and for some reason feel threatened when those principles are abstracted away, as if their knowledge is now wasted. But that's the natural progression of things. Sorry. It's funny it happens in a community of programmers though, where half of the tools that are used everyday are blackboxes that few really understand. Like the computer itself.
- flipgimble 10y ago
  And its completely fine to be the developer who uses pre-made algorithmic block for their specific problem. However you will always be several years behind the current state of art. For example deep-learning really revolutionized the state of the art in image recognition in 2012 by winning academic competitions. It took about 3-5 years for those deep learning algorithms to get productized into packages like tensorflow, with high production tutorials and videos, so it was accessible to non-academics. I don't think people that know the underlying principles of machine learning are threatened (Thats sounds like pretty insecure world view on your part). They operate in a different context where you want to push the state of the art in machine learning algorithms, instead of just applying existing best-practices to your specific problem.
  - forgetsusername 10y ago
    >However you will always be several years behind the current state of art I agree with your post, but 99.9% of people who will be applying ML via black-box algorithm in the next decade won't be participating in, or at all concerned with, the state-of-the-art. In the same way that most of us aren't concerned about state-of-the-art chip design. I can do a regression analysis with a couple clicks in excel. I need little knowledge beyond how to interpret results. Sure, the underlying data might violate some assumptions, but it's rare (and there are tools for that). And let's face it, the most popular applications by amateurs will be marketing related, not cancer-curing related.
    - 10y ago