Machine learning in physics: a short guide

This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, and deep learning. We present some of the principal applications of machine learning in physics and discuss the associated challenges and perspectives. Ernest Rutherford once declared: "if your experiment Also, generative modelling offers a way to discern the needs statistics, you ought to have done a better experiment" most credible theory from various explanations for observational [1]. His remark reflects his belief in the significance data. This is achieved solely through the data, of well-controlled experiments and the need for experimental without any predetermined understanding of the potential designs that minimize uncertainties and sources of physical mechanisms operating within the studied system errors. However, while Rutherford's statement may have [18]. Therefore, the possibilities for using ML algorithms merit in his time, it no longer applies in the modern scientific in physics range from experiments to theoretical landscape.