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I think you're glossing over the fact that for a non-trivial problem, you have the pick the right machine learning algorithm from a large set of possibilies, an

I think you're glossing over the fact that for a non-trivial problem, you have the pick the right machine learning algorithm from a large set of possibilies, and typically you have to set a number of parameters to make it work. On top of that you have to follow proper methodology to get meaningful results; e.g., to determine whether the accuracy you're getting generalizes to other data as well. I think all of this contributes to making applied machine learning a non-trivial skill. And indeed, AI is just the right background for that, but the point is that you need such a background.

diab0lic 13y ago
Thats true, though as my optimization friends put it, sometimes a really close answer that is easy to come to is often better than a precise answer which is difficult to come to. Selecting the right algorithm is a bit of an art though some simple rules can make it pretty easy. Say clustering for groups, linear regression for estimating continuous values, naive Bayes / logistic regression for classification. Without parameter tuning you can go from having a bunch of data to having a model that does something quite easily. When were talking about applied machine learning it is generally in the context of developing a product, a feature. The most important part is going from data to feature, not necessarily squeezing the last few percentage points of performance. In this context getting a good answer easily is more important than getting the best answer.
deleted 13y ago
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- andreasvc 13y ago
  The combinatorial number of possibilities quickly grows beyond what can be tested automatically, so you have to start from good intuitions. That's actually what machine learning is _not_ great at; it finds patterns in data (maybe even previously unknown ones), but arguably doesn't come up with creative insights.

diab0lic 13y ago

Thats true, though as my optimization friends put it, sometimes a really close answer that is easy to come to is often better than a precise answer which is difficult to come to. Selecting the right algorithm is a bit of an art though some simple rules can make it pretty easy. Say clustering for groups, linear regression for estimating continuous values, naive Bayes / logistic regression for classification. Without parameter tuning you can go from having a bunch of data to having a model that does something quite easily. When were talking about applied machine learning it is generally in the context of developing a product, a feature. The most important part is going from data to feature, not necessarily squeezing the last few percentage points of performance. In this context getting a good answer easily is more important than getting the best answer.

deleted 13y ago

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andreasvc 13y ago
The combinatorial number of possibilities quickly grows beyond what can be tested automatically, so you have to start from good intuitions. That's actually what machine learning is _not_ great at; it finds patterns in data (maybe even previously unknown ones), but arguably doesn't come up with creative insights.