A brief introduction to machine learning for engineers

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30 Scopus citations

Abstract

This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and unsupervised learning problems. It introduces fundamental concepts and algorithms by building on first principles, while also exposing the reader to more advanced topics with extensive pointers to the literature, within a unified notation and mathematical framework. The material is organized according to clearly defined categories, such as discriminative and generative models, frequentist and Bayesian approaches, exact and approximate inference, as well as directed and undirected models. This monograph is meant as an entry point for researchers with an engineering background in probability and linear algebra.

Original languageEnglish (US)
Pages (from-to)200-431
Number of pages232
JournalFoundations and Trends in Signal Processing
Volume12
Issue number3-4
DOIs
StatePublished - 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing

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