ArticlePhysics reports2019
A high-bias, low-variance introduction to Machine Learning for physicists.
Article in Physics reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
129 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2020Pooled it
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- Telomeric G-quadruplex intermediates unveiled by complex Markov network analysis.Scientific reports · 2026Article
- Machine learning prediction of multiple distinct high-affinity chemotypes for α-synuclein fibrils.Chemical communications (Cambridge, England) · 2026Article
- Quantum informative analysis for weather of San Francisco.Scientific reports · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- Unsupervised classification of non-Hermitian topological phases under symmetries.National science review · 2026Article
- Deep Learning of the Biswas-Chatterjee-Sen Model.Entropy (Basel, Switzerland) · 2025Article
- Nonlinear memory in cell-division dynamics across species.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Physically Consistent Self-Diffusion Coefficient Calculation with Molecular Dynamics and Symbolic Regression.International journal of molecular sciences · 2025Article
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- Machine learning models for delineating marine microbial taxa.NAR genomics and bioinformatics · 2025Article
- Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants.Scientific reports · 2025Article
- Force spectroscopy reveals membrane fluctuations and surface adhesion of extracellular nanovesicles impact their elastic behavior.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
69 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists may be able to contribute.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.