Principled machine learning

Yordan Raykov, David Saad

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Number of pages19
JournalIEEE Journal of Selected Topics in Quantum Electronics
Early online date27 Jun 2022
DOIs
Publication statusE-pub ahead of print - 27 Jun 2022

Bibliographical note

UKRI Rights Retention: For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising

Funding: DS acknowledges support from the EPSRC Programme Grant TRANSNET (EP/R035342/1) and the Leverhulme trust (RPG-2018-092). YR acknowledges support by the EPSRC Horizon Digital Economy Research grant ‘Trusted Data Driven Products: EP/T022493/1 and grant ‘From Human Data to Personal Experience’: EP/M02315X/1.

Keywords

  • Channel estimation
  • Computational modeling
  • Kernel
  • Machine learning
  • Neural networks
  • Probabilistic logic
  • Statistical machine learning
  • Visualization
  • deciion trees
  • dimensionality reduction
  • kernel-based methods
  • message passing techniques
  • probabilistic methods
  • visual informatics

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