Machine learning can be quite jargon-heavy, and in my experience the core ideas can often be hidden amongst a bunch of unnecessary maths. I got frustrated by a

Machine learning can be quite jargon-heavy, and in my experience the core ideas can often be hidden amongst a bunch of unnecessary maths. I got frustrated by a paper yesterday which contained function definitions, summations-of-summations, products of sequences, convolutions, set theory, switching back-and-forth between unary-functions/vectors and binary-functions/matrices, converting back-and-forth between {0, 1}, {-1, 1} and {true, false}, weighting elements of a set by 0/1 instead of taking a sub-set, linear programming, etc. What was their result? To speed up pair-wise comparisons of structured data, only do N% of the comparisons and it will only take N% of the time. To decide which comparisons to discard, see what works well on a small sample of inputs.