Statistics and linear algebra for ML

Linear algebra is a valuable tool in other branches fo mathematics, especially statistics.

The impact of linear algebra is important to consider, given the foundational relationship both fields have with the field of applied machine learning. Sime points of linear algebra in statistics and statistical methods are as follows-

1. Use of vector and matrix notation, especially with multivariate statistics. 
2. Solutions to least squares and weighted least squares, such as for linear regression.
3. Estimates of mean and variance of data matrices.
4. The covariance matrix plays a key role in multinomial Gaussian distributions. 
5. Principal component analysis for data reduction that draws many of these elements together.

As we can see, modern statistics and data analysis =, at least as far as interests of a machine learning practitioner are concerned, depend on the understanding and tools of linear algebra.