Machine Learning Methods for Understanding Physics

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• David Saad (Aston)
• Thursday 31 January 2019, 13:45-15:00
• Theory Library.

If you have a question about this talk, please contact Mike Gunn.

Machine learning is a colloquial term that encompasses a collection of data-driven methods of various types that aim at understanding, inferring and optimising systems. This includes inferring the state of individual system variables, the interaction strengths between them and the system’s characteristic phases. The recent excitement around engineering successes of machine learning lead to their application in tackling fundamental questions in physics, especially the interpretation and inference of experimental data. These successes suggest that machine learning techniques may become a standard tool in physics research [1]. In this talk I will review existing machine learning techniques and motivate the use of principled probabilistic approaches, while explaining recent high-profile heuristics and their limitations.

[1] L. Zdeborová, Machine learning: New tool in the box, Nature Physics 13, 420EP (2017)

This talk is part of the Theoretical Physics Seminars series.

This talk is included in these lists:

• Bham Talks
• Theoretical Physics Seminars
• Theory Library
• What's on in Physics?

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