This is our Bayeisan Machine Learning textbook, with a PDF for the book and accompanying Python notebooks. The goal of this book is to provide a practical but thorough introduction to Bayesian Machine Learning. Bayesian methods provide theoretically supported regularization using priors, methods for inference of distributions and relationships between them, and uncertainty quantificaiton on predictions.

All examples and figures that involved programming have an associated Python notebook that is provided here. The notebooks are also included in the appendix of the textbook PDF file. These materials were written for the UVA Bayeisan Machine Learning course. The prerequisites are some knowledge of Python programming, a course in probability/statistics including linear and logistic regression, and some knowledge of machine learning will be helpful in some topics. However, a reasonable effort is made in the text to provide background/review concepts. Materials will be updated here as they are developed.