Today we’re talking about linear least squares curve fitting, but don’t be fooled by the name. I’ll show how to use it to fit polynomials and other functions, how to derive it, and how to calculate it efficiently using a Cholesky matrix decomposition. Continue reading Least Squares Curve Fitting →
Assume a distribution and conditional independence, calculate means and standard deviations, and use it to make predictions. It’s about the simplest thing that qualifies as machine learning. I’m not really a fan, but we’ve got to start somewhere, right? Continue reading Naive Bayes →
Today we’re talking about linear least squares curve fitting, but don’t be fooled by the name. I’ll show how to use it to fit polynomials and other functions, how to derive it, and how to calculate it efficiently using a Cholesky matrix decomposition. Continue reading Least Squares Curve Fitting 