Logo

An Introduction to Probabilistic Programming

Small book cover: An Introduction to Probabilistic Programming

An Introduction to Probabilistic Programming
by

Publisher: arXiv.org
Number of pages: 218

Description:
This document is designed to be a first-year graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and programming languages.

Home page url

Download or read it online for free here:
Download link
(3.4MB, PDF)

Similar books

Book cover: Introduction to Machine Learning for the SciencesIntroduction to Machine Learning for the Sciences
by - arXiv.org
This is an introductory machine learning course specifically developed with STEM students in mind, written by the theoretical Condensed Matter Theory group at the University of Zurich. We discuss supervised, unsupervised, and reinforcement learning.
(5491 views)
Book cover: Foundations of Machine LearningFoundations of Machine Learning
by - The MIT Press
This is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools.
(8848 views)
Book cover: Introduction to Machine LearningIntroduction to Machine Learning
by - Cambridge University Press
Over the past two decades Machine Learning has become one of the mainstays of information technology and a rather central part of our life. Smart data analysis will become even more pervasive as a necessary ingredient for technological progress.
(12514 views)
Book cover: Statistical Foundations of Machine LearningStatistical Foundations of Machine Learning
by
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. This manuscript aims to find a good balance between theory and practice.
(11463 views)