Principled Machine Learning

Raykov, Yordan P.; Saad, David

Abstract

We introduce the underlying concepts which give rise to some of the commonly used machine learning methods, excluding deep-learning machines and neural networks. We point to their advantages, limitations and potential use in various areas of photonics. The main methods covered include parametric and non-parametric regression and classification techniques, kernel-based methods and support vector machines, decision trees, probabilistic models, Bayesian graphs, mixture models, Gaussian processes, message passing methods and visual informatics.

Citation

Raykov, Y. P., & Saad, D. (2022). Principled Machine Learning. IEEE Journal of Selected Topics in Quantum Electronics, 28(4), Article 0200419. https://doi.org/10.1109/JSTQE.2022.3186798

• Journal Article Type: Article
• Acceptance Date: Mar 1, 2022
• Online Publication Date: Jun 27, 2022
• Publication Date: 2022-07
• Deposit Date: Feb 7, 2025
• Print ISSN: 1077-260X
• Electronic ISSN: 1558-4542
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 28
• Issue: 4
• Article Number: 0200419
• DOI: https://doi.org/10.1109/JSTQE.2022.3186798
• Public URL: https://nottingham-repository.worktribe.com/output/45043321
• Publisher URL: https://ieeexplore.ieee.org/document/9808310