For the rest of this course, we are going to focus on building a working knowledge of matrix math, which is also known as linear algebra. But what do I mean by working knowledge? As Sebastian has repeatedly said, it is not important to memorize equations, but as a self-driving car engineer, it will be important for you to be able to find and use the equations you need when you need to use them. So imagine that at some point in the future, you’re working on the traffic protection team for some self-driving car company, and you’re trying to build a prototype of some object tracking code. You might create a file called, I don’t know, Kalman filter dot pi, and then, well, if you’re like most people, you probably have to go to Google. And once you’re there, you’d start typing in Kalman filter, and you’d see a list of results. And, as you can tell, I personally like Wikipedia. And what you’d see would look something like this. And since you wouldn’t be looking to look through all the theory, you’d probably scroll right down to the common filter equations, which looks something like this. And, unsurprisingly, there is separated entry predict and an update step. And you probably have an understanding of what those mean conceptually. But that doesn’t necessarily mean you could do anything with all these equations, and that’s kind of a shame. So the goal for this course and the project that falls is to make sure that when you see equations that look like these, you’ll be able to put them to use in the future. What that means is you can translate the matrix math that you see in a page like Wikipedia into real code. So let’s begin.