1 Introduction

You’ve typically heard something like this before: “in order to have a solid understanding of statistical learning methods, you need a good knowledge of matrix algebra.” Which I agree with. Matrix algebra is fundamental for a good understanding of Statistical Learning methods, Machine Learning methods, Data Mining techniques, as well as Multivariate Data Analysis methods (keep in mind that there is a considerable amount of overlap in all these fields).

What you probably haven’t heard is that “knowing matrix algebra is not (really) enough.” You also need to learn how various matrix algebra concepts are connected with the ideas and notions behind statistical learning methods.

In my opinion, the three main reasons for why you should bother learning about matrix algebra are:

1. Multivariate data is commonly represented in tabular format (rows and columns).

2. Mathematically, a data table can be treated as a matrix.

3. Matrix algebra provides the analytical machinery and tools to manipulate and exploit values, information, and patterns of variability in data.