# Math Notes

• ## Simple Linear Regression

Simple linear regression is the process of identifying the linear function (non-vertical straight line) which best describes the relationship between one independent and one dependent variable.

Factorising an equation is the reverse procedure to expanding brackets. It’s GCSE level math and is definitely something most people who use math on a regular basis should be able to do.

• ## A Short Intro To Differentiation

Differentiation is the process of computing the gradient (derivative) of an arbitrary function and can be used in many simple cases to minimise a function describing the loss (error) of our model.

• ## Notes On Notation

Sometimes mathematical notation can seem a bit opaque, to the point where the mathematical notation used in a paper is usually noted at the beginning of the text. These are the relevant ones for the math I’ve used.

• ## The Types and Uses of Variables

If it can take on different values, it’s a variable. They come in a variety of flavours and are extremely important in experimental design.

• ## The Confusion Matrix

A confusion matrix is a simple way to visually present the accuracy of a classification algorithm.

• ## The Cosine Rule and Dot Product

This is a generalisation of Pythagoras’ theorem to apply to all triangles rather than just right angled ones. The cosine rule reduces to Pythagoras’ Theorem as well as providing the mathematical basis behind the usefulness of the dot product for establishing the extent to which two vectors are going in the same direction.

• ## Euclidean Distance

Euclidean Distance is the ‘ordinary’ straight line distance between two points in Euclidean Space. It can be seen in action as the frustrating difference in distance between how far away something is (the straight line distance) and how far you have to go to get there (the rather disappointingly named distance travelled).