An Introduction to Machine Learning: a perspective from Statistical Physics

被引:10
作者
Decelle, Aurelien [1 ,2 ]
机构
[1] Univ Complutense, Dept Fis Teor, Madrid 28040, Spain
[2] Univ Paris Saclay, INRIA Tau team, CNRS, LISN, F-91190 Saclay, France
关键词
Machine Learning; Perceptron; Restricted Boltzmann Machine; Phase diagram; NETWORK; MODEL; STORAGE;
D O I
10.1016/j.physa.2022.128154
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The recent progresses in Machine Learning opened the door to actual applications of learning algorithms but also to new research directions both in the field of Machine Learning directly and, at the edges with other disciplines. The case that interests us is the interface with physics, and more specifically Statistical Physics. In this short lecture, I will try to present first a brief introduction to Machine Learning from the angle of neural networks. After explaining quickly some fundamental models and global aspects of the training procedure, I will discuss into more detail two examples illustrate what can be done from the Statistical Physics perspective. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页数:31
相关论文
共 50 条
[41]   Machine learning applications to computational plasma physics and reduced-order plasma modeling: a perspective [J].
Faraji, Farbod ;
Reza, Maryam .
JOURNAL OF PHYSICS D-APPLIED PHYSICS, 2025, 58 (10)
[42]   Network meta-analysis: a statistical physics perspective [J].
Davies, Annabel L. ;
Galla, Tobias .
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2022, 2022 (11)
[43]   Introduction to Supervised Machine Learning [J].
Biswas, Aditya ;
Saran, Ishan ;
Wilson, F. Perry .
KIDNEY360, 2021, 2 (05) :878-880
[44]   Introduction to Machine Learning for Ophthalmologists [J].
Consejo, Alejandra ;
Melcer, Tomasz ;
Rozema, Jos J. .
SEMINARS IN OPHTHALMOLOGY, 2019, 34 (01) :19-41
[45]   An introduction to quantum machine learning [J].
Schuld, Maria ;
Sinayskiy, Ilya ;
Petruccione, Francesco .
CONTEMPORARY PHYSICS, 2015, 56 (02) :172-185
[46]   Facial Expression Recognition in Educational Research From the Perspective of Machine Learning: A Systematic Review [J].
Fang, Bei ;
Li, Xian ;
Han, Guangxin ;
He, Juhou .
IEEE ACCESS, 2023, 11 :112060-112074
[47]   Statistical-Physics-Informed Neural Networks (Stat-PINNs): A machine learning strategy for coarse-graining dissipative dynamics [J].
Huang, Shenglin ;
He, Zequn ;
Dirr, Nicolas ;
Zimmer, Johannes ;
Reina, Celia .
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS, 2025, 194
[48]   Machine Learning Monte Carlo Approaches and Statistical Physics Notions to Characterize Bacterial Species in Human Microbiota [J].
Bellingeri, Michele ;
Mancabelli, Leonardo ;
Milani, Christian ;
Lugli, Gabriele Andrea ;
Alfieri, Roberto ;
Turchetto, Massimiliano ;
Ventura, Marco ;
Cassi, Davide .
MACHINE LEARNING AND KNOWLEDGE EXTRACTION, 2024, 6 (04) :2375-2399
[49]   Learning Quantum Drift-Diffusion Phenomenon by Physics-Constraint Machine Learning [J].
Li, Chun ;
Yang, Yunyun ;
Liang, Hui ;
Wu, Boying .
IEEE-ACM TRANSACTIONS ON NETWORKING, 2022, 30 (05) :2090-2101
[50]   Access to online learning: Machine learning analysis from a social justice perspective [J].
Nora A. McIntyre .
Education and Information Technologies, 2023, 28 :3787-3832