So far, you’ve learned a lot about how to extract important information from images and how to use deep learning techniques to recognize patterns in images and identify objects. In this part of the course, we’ll focus less on images and more on pattern recognition over time and space. We’ll talk about different computer vision methods for tracking objects and predicting their movement. In fact, in this lesson, you’ll be programming a filter that tracks a robot as it moves and senses its environment over time. This requires having a model of motion and the mathematical way to represent uncertainty in that motion. Finding out where a moving object is located with some certainty at any given time is called localization. Localization techniques are used extensively in computer graphics applications that track certain objects and people over time, and localization is used in autonomous vehicles, like in self-driving cars that need to know where they are in the world so that they can safely navigate. Next, let’s talk about the specific skills that we’ll cover in the next few lessons.