A Brief Introduction to Machine Learning for Engineers

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.

**Tag(s):**
Machine Learning

**Publication date**: 17 May 2018

**ISBN-10**:
168083472X

**ISBN-13**:
978168083472

**Paperback**:
237 pages

**Views**: 8,342

A Brief Introduction to Machine Learning for Engineers

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.

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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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About The Author(s)

Osvaldo Simeone is a Professor of Information Engineering with the Centre for Telecommunications Research at the Department of Informatics at King's College, London. He received an M.Sc. degree (with honors) and a Ph.D. degree in information engineering from the Politecnico di Milano, Italy, in 2001 and 2005, respectively. He was previously a Professor of Electrical Engineering at the New Jersey Institute of Technology (NJIT) in Newark.

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Computer Science
Introduction to Computer Science
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