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A Brief Introduction to Machine Learning for Engineers

  • Category: Computer
  • Author: Osvaldo Simeone
  • License: arXiv.org – Non-exclusive license to distribute
  • File type: PDF (206 pages), PostScript. DVI, etc.

Read and download free eBook intituled A Brief Introduction to Machine Learning for Engineers in format PDF (206 pages), PostScript. DVI, etc. created by Osvaldo Simeone.

This book aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference. The intended readership consists of electrical engineers with a background in probability and linear algebra.

The treatment builds on first principles, and organizes the main ideas according to clearly defined categories, such as discriminative and generative models, frequentist and Bayesian approaches, exact and approximate inference, directed and undirected models, and convex and non-convex optimization. The text offers simple and reproducible numerical examples providing insights into key motivations and conclusions.

The mathematical framework uses information-theoretic measures as a unifying tool.

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A Brief Introduction to Machine Learning for Engineers

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