[MUSIC] Welcome to week one of the Estimation and Learning Robotics module. This week with Bhoram Lee, we'll be learning about how to use Gaussian models for estimation and learning. First of all, we will learn about one dimensional Gaussian distributions, which is the canonical model to measure, and express uncertainty in distributions. We'll learn how to use these one-dimensional Gaussian Distributions for estimation and for maximum likelihood estimation. Next, we'll look at Multivariate Gaussian Distributions, which are the generalization to higher dimensions of Gaussians and we'll learn about the mean covariance properties of these distributions. Finally, we'll learn about Gaussian Mixture Models, which are the combinations of Gaussians to model a more complex distributions. To summarize, this week, you will be learning how to use these different types of Gaussian Distributions to learn and estimate from data. >> Hi, my name is Bhoram Lee. I'm a PhD student in the GRASP Lab at the University of Pennsylvania. I am studying robotic perception and learning for my research. I am happy to introduce important and interesting topics in robotics, such as Gaussian models and robotic mapping to you.