A Brief Introduction to Machine Learning for Engineers (Osvaldo Simeone)

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A Brief Introduction to Machine Learning for Engineers (Osvaldo Simeone)

Statistical learning theory, probabilistic graphical models, supervised and unsupervised learning, and approximation inference are just a few of the fundamental ideas, algorithms, and theoretical frameworks that this book seeks to introduce.

Electrical engineers with a foundation in probability and linear algebra make up the target audience.

The discussion starts from the ground up and groups the key concepts into distinct categories, such as discriminative and generative models, frequentist and Bayesian techniques, exact and approximative inference, directed and undirected models, and convex and non-convex optimization. The article provides straightforward, reproducible numerical examples that shed light on the main assumptions and conclusions.

Information-theoretic metrics are used as a unifying mechanism in the mathematical framework.

Rather than providing exhaustive details on the existing myriad solutions in each specific category, for which the reader is referred to textbooks and papers, this book is meant as an entry point for an engineer into the literature on machine learning.

Ebook Details

About the Authors
Osvaldo Simeone is a Professor of Information Engineering in the Department of Informatics, King's College, London, UK.
Published Date / Year
(May 17, 2018)
arXiv.org - Non-exclusive license to distribute
eBook Format
PDF (237 pages), PostScript. DVI, etc.

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