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I've been doing machine learning. I have done lots of learning related to data analysis and ML algorithms, and I've got very good results and I understand the algorithms. However, my approaches are normally getting datasets, writing scripts or notebooks to play around with the data to try to build a relatively accurate model and test the accuracy and that's pretty much it. Which means, no matter how accurate I come up with a solution, I have to run the script everytime for each update, this is not enough. In this case the solutions only stay statically in the script.

But machine learning is way more than that, right?

I think that machine learning is more than that, and there are already smart systems that are constantly updating their systems automatically, and I am really interested in that, but can't really find any good materials that dive deep into that field.

Let's just say, like the classic example, there's a website that has a ML system at the backend that identifies iris categories, and it's visitors constantly upload new observations of irises and the visitors get the category of the iris they upload, and the backend system continues to improve accordingly.

So my questions would be:

1. How can I get to learn thing related to building a smart/constantly learning & updating system?

2. Is there any materials that are related to the fields beyond just "get data -> build model -> get accuracy"?

I'd really appreciate all your opinions!

Thanks a lot!

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  • $\begingroup$ Bayesian statistics might be worth looking into. There are often sequential/recursive formulas for updating the posterior distribution. Computationally, every time you get a new data point, you would revise the existing posterior distribution sitting in memory. However, this is not the only area that has recursive formulas. $\endgroup$ – Taylor Apr 28 '17 at 5:59
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Read more about:

  • Active Learning Algorithms
  • Online Machine Learning Algorithms
  • Semi-Supervised
  • Add triggering system to ML framework
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