In terms of AI/deep learning and data science as well as advanced robotics design, there are a handful of advanced mathematics areas that one is told to study.
These areas are linear algebra, calculus, probability and statistics.
You must know these mathematics subjects if you are deeply involved in AI and data science because you will be dealing with massive amounts of data and numbers which must be organized and undestood with specialized mathematical techniques.
You don’t need to know much math to simply use pre written code libraries such as the Open CV software which deals with vision systems and AI since much of it has already been written.
Now in this section we are going to explain some of the basics of linear algebra.
Basically linear algebra is an advanced mathematics topic taught to science and engineering students in their first year which deals with the study of linear systems of equations, vector spaces and more.
Linear means in a straight line
It is used by scientists to design and analzye complex systems and in building bridges which is also the case with calculus.
Now when you start looking into the subject of deep learning you will be surrounded by massive amounts of data and situations where one group of data is compared to another and they are lined up in rows and columns called matrices.
Now matrixes or matrices which we will soon define are very complex and are used in vision systems and software like Gan and facial recognition software where the volumes of numbers which describe a face is lined up in matrixes and compared to other matrixes describing other faces.
So linear algebra is involved in analazing and finding patterns etc.
You will come across a very important term in datra science and AI called optimization which we will also soon try to define.
You will hear talk of simple single line linear algebra and multivariate calculus until your head explodes.
But remember you really don’t need to know anything about this area of advanced mathematics unless you will really get deeply involved in AI or robotics design and you are probably already enrolled in Engineering courses so this is basically baby talk to you anyhow:)