> The link goes even deeper than that. Cosines have an intimate relationship with Euclidean distance, and the fundamental statistics concept of variance is in turn intimately related to Euclidean distance... and Gaussian distributions (perhaps the most continuous distribution family because of the Central Limit Theorem) are parameterized directly by mean and variance. And Gaussian models have a convenient habit of reducing to straightforward linear algebra as a result. The fact that Gaussian problems also happen to be nicely differentiable is yet another bonus. Oh, and least-squares linear regression (read: fit a line to optimize the Euclidean distance between your prediction vector and the data) is equivalent to a Gaussian maximum likelihood model.

Thanks for this insight! Can you also suggest a book or other materials that I can read to understand this in greater depth?