A Brief Introduction to Machine Learning for Engineers
It serves as an entry point for researchers with a background in probability and linear algebra, offering a foundational overview rather than tackling a specific problem.
This monograph provides an introduction to key concepts, algorithms, and theoretical results in machine learning, focusing on probabilistic models for supervised and unsupervised learning problems, with material organized into categories like discriminative and generative models.
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.