A Brief Introduction to Machine Learning for Engineers

Understand

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.