| Document Type
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BL
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| Record Number
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855214
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| Main Entry
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Simeone, Osvaldo
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| Title & Author
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A brief introduction to machine learning for engineers /\ Osvaldo Simeone.
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| Publication Statement
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Boston :: now,, [2018]
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, ©2018
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| Series Statement
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Foundations and trends in signal processing ;; vol. 12, no. 3-4
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| Page. NO
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1 online resource (pages [200]-431) :: illustrations.
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| ISBN
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168083472X
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: 9781680834727
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| Bibliographies/Indexes
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Includes bibliographical references (pages 418-431).
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| Contents
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Introduction -- A gentle introduction through linear regression -- Probabilistic models for learning -- Classification -- Statistical learning theory -- Unsupervised learning -- Probabilistic graphical models -- Approximate inference and learning -- Concluding remarks.
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| Abstract
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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.
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| Subject
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Bayesian statistical decision theory.
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Graphical modeling (Statistics)
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Machine learning.
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| Subject
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Bayesian statistical decision theory.
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Graphical modeling (Statistics)
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| Subject
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Machine learning.
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| LC Classification
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TK5102.9.F69 v.12, no.3-4
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