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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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(p.1) 1 Introduction
Fundamentals of Machine Learning

Thomas P. Trappenberg

Oxford University Press

This chapter provides a high-level overview of machine learning, in particular how it is related to building models from data. It starts with placing the basic concept in its historical context and phrases the learning problem in a simple mathematical term as function approximation as well as in a probabilistic context. In contrast to more traditional models, machine learning can be characterized as non-linear regression in high-dimensional spaces. This chapter points out how diverse subareas such as deep learning and Bayesian networks fit into the scheme of things and motivates further study with some examples of recent progress.

Keywords:   function approximation, non-linear regression, probabilistic context, historical context, mathematical formulation

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