Free Lecture content on Probabilistic Machine Learning Series(Work in Progress!) Dear Folks, sharing Lecture 11 of our Machine Learning series, and this is a bit special to me, because today I cover Conditionals of Multivariate Normals, and Linear Gaussian Systems. When I first started studying these topics, it took me days to understand. But today I have made a lecture on it, so if you understand the concepts, it’s really good, for I have tried to leave no stone unturned while explaining, deriving the equations, doing it step by step, and tried giving all intuitions I could. The Gaussian distribution is ubiquitous and important in studying topics as state estimation, tracking, and examples include Autonomous vehicles, robotics and navigation, time-series forecasting, aerospace etc. The breakdown is as: 0-10: Marginals and Conditionals of Multivariate Normals, Matrix Inversion Rules 10-27: Derivation of the Matrix Inverse Rule: Schur Complements(We need this to derive equations for Multivariate Gaussian) 27-45: Deriving the Conditionals of MVN 45-1:03: Example and Imputation of Missing Values 1:03-1:47: Linear Gaussian Systems, and full derivation of Bayes Rule for Gaussians. 1:47-2:19: Inferring an Unknown Scalar and Sequential Updates. 2:19-2:34: Inferring an Unknown vector. 2:37-End: Sensor Fusion. This lecture is relatively bigger since the concepts are interrelated here. But do not worry, I have tried to explain in the best way I could, and hope it helps you well in your journey to becoming a Machine learning engineer. Link in shared in comments. submitted by /u/Negative_War_65
Originally posted by u/Negative_War_65 on r/ArtificialInteligence
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