https://en.wikipedia.org/wiki/Outline_of_statistics
Terms
sample, unbiased (representative) sample
sample mean, average $\bar x$
sample covariance, correlation
point estimate, confidence interval at confidence level $\gamma$ (also called confidence coefficient)
the higher level $\gamma$ we want, the wider the confidence interval we get
https://en.wikipedia.org/wiki/Type_I_and_type_II_errors: type I error is false positive (rejection of true null hypothesis), type II error is false negative (failure to reject a false null hypothesis)
Linear regression
and non-linear regression, quadratic function is the simplest example
Stohastic gradient descent
Least squares square method involves operations on matrices which can be huge.
Mini-batch SGD.
https://stats.stackexchange.com/questions/160179/do-we-need-gradient-descent-to-find-the-coefficients-of-a-linear-regression-mode
https://stats.stackexchange.com/questions/23128/solving-for-regression-parameters-in-closed-form-vs-gradient-descent