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Fundamentals of Machine Learning$
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Thomas P. Trappenberg

Print publication date: 2019

Print ISBN-13: 9780198828044

Published to Oxford Scholarship Online: January 2020

DOI: 10.1093/oso/9780198828044.001.0001

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Probabilistic regression and Bayes nets

Probabilistic regression and Bayes nets

Chapter:
(p.141) 7 Probabilistic regression and Bayes nets
Source:
Fundamentals of Machine Learning
Author(s):

Thomas P. Trappenberg

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198828044.003.0007

This chapter revises regression with the inclusion of uncertainty in the data in probabilistic models. It shows how modern probabilistic machine learning can be formulated. First, a simple stochastic generalization of the linear regression example is offered to introduce the formalism. This leads to the important maximum likelihood principle on which learning will be based. This concept is then generalized to non-linear problems in higher dimensions and the chapter relates this to Bayes nets. The chapter ends with a discussion about how such a probabilistic approach is related to deep learning.

Keywords:   probabilistic models, Bayesian nets, maximum likelihood, map, regularization, causal models

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