This monograph aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference. The intended readership consists of electrical engineers with a background in probability and linear algebra. The treatment builds on first principles, and organizes the main ideas according to clearly defined categories, such as discriminative and generative models, frequentist and Bayesian approaches, exact and approximate inference, directed and undirected models, and convex and non-convex optimization. The mathematical framework uses information-theoretic measures as a unifying tool. The text offers simple and reproducible numerical examples providing insights into key motivations and conclusions. Rather than providing exhaustive details on the existing myriad solutions in each specific category, for which the reader is referred to textbooks and papers, this monograph is meant as an entry point for an engineer into the literature on machine learning.

本文旨在介绍机器学习的关键概念、算法及理论结果，聚焦于监督/无监督学习问题的概率模型并通过基础概念和算法入手，引导读者接触更深入的课题，并提供指向文献的引用；按照判别模型和生成模型、频率学派和贝叶斯学派、精确和近似推理以及有向和无向模型等明确定义分类。此篇论文旨在为在概率和线性代数方面具有背景的研究者提供进入该领域的入门资料。

工程师的机器学习简介