Gaussian Processes

For Machine Learning, Gaussian Processes enable flexible models with the richest output you could ask for - an entire predictive distribution (rather than a single number). In this video, I break down what they are, how they work and how to model with them. My intention is this will help you join the large group of people successfully applying GPs to real world problems. SOURCES Chapter 17 from [2] is the most significance reference for this video. That's where I discovered the Bayesian Linear Regression to GP generalization, the list of valid ways to adjust a kernel and the Empirical Bayes approach to hyperparameter optimization. Also, it's where I get most of the notation. (In fact, for *all* my videos, Kevin Murphy's notation is what I follow most closely.) [1] is a very thorough practical and theoretical analysis of GPs. When I first modeled with GPs, this book was a frequent reference. It offers a lot of practical advice for designing kernels, hyperparameter optimization and interpreting results. [5]