In ML applications, we often have high-dimensional data. If we're recording 50 different metrics for each of our shoppers, we're working in a space with 50 dimensions. If we're analyzing grayscale images sized 100 x 100, we're working in a space with 10,000 dimensions. If the images are RGB-colored, the dimensionality increases to 30,000 dimensions (one dimension for each color channel in each pixel in the image)!
This problem is called the curse of dimensionality. On one hand, ML excels at analyzing data with many dimensions. Humans are not good at finding patterns that may be spread out across so many dimensions, especially if those dimensions are interrelated in counter-intuitive ways. On the other hand, as ...
