What exactly is machine learning and how does it work?
In: Technology
In the 90s, what is now called “machine learning” was once called “statistical learning”. It’s basically an umbrella term for any model that can “learn” as you feed it data – you don’t have to teach it (write code to do what you want it to do), you just tell it how to learn
The simplest archetype of ML is “classifiers”, which take an input (say, a picture) and yield and output (“does this image contain a cat – yes or no”). The non-ML approach is manually writing code to look at all the little criteria in the image and telling the model exactly how to act. The ML approach is writing a structure (such as an SVM or neural network) that can figure it out on its own if you train it with a bunch of images and label the images in the training data as “cat” or “non-cat”; the model starts with its own random set of rules to discriminate between cat images and non-cat images and gradually updates its rules to make more accurate predictions
The big “thing” is that you aren’t updating the model yourself – you tell the model how to learn and it updates itself on its own based on how you “told it how to learn”
If this is interesting to you, the math isn’t too hard, but it does require a strong linear algebra background (and a little calculus)
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