An Introduction to Machine Learning: a perspective from Statistical Physics

Aurélien Decelle

Physica A: Statistical Mechanics and its Applications, 2023, vol. 631, issue C

Abstract: 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.

Keywords: Machine Learning; Perceptron; Restricted Boltzmann Machine; Phase diagram (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:631:y:2023:i:c:s0378437122007129

DOI: 10.1016/j.physa.2022.128154

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