A Brief Introduction to Machine Learning for Engineers

TL;DR

This paper provides an accessible introduction to core machine learning concepts, algorithms, and theoretical foundations, focusing on probabilistic models for supervised and unsupervised learning, suitable for researchers with a probability and linear algebra background.

Contribution

It offers a comprehensive, unified overview of fundamental and advanced machine learning topics with clear organization and extensive literature pointers.

Findings

Clarifies key probabilistic models and algorithms

Integrates various approaches within a unified framework

Serves as an entry point for researchers in the field

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 a background in probability and linear algebra.