A Brief Introduction to Machine Learning for Engineers

This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learn- ing. The treatment concentrates on probabilistic models for supervised and unsupervised learning problems. It in- troduces fundamental concepts and algorithms by buildingon 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 monographis meant as an entry point for researchers with an engineer- ing background in probability and linear algebra.