Machine (Deep) Learning for physicists 2025-26 (EUtopia)
Module Convenor: Rudo Roemer (U Warwick) and Mats Granath (U Gothenburg)
- Times: Mondays 16-17 UK/17-18 CET and Wednesdays 16-17 UK/17-18 CET
- 1st (online) lecture: Wednesday, January 14th, 2026
- Course duration: MPAGS + CY Cergy-Paris Université, 6 weeks of lectures, 5 weeks of project work
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The course is for the post-graduate students of the EUtopia Alliance (such as those on the CY Cergy-Paris Universite M2-level Postgraduate course 2025-26) and all interested students of the Midlands Physics Alliance Graduate School.
Machine learning and deep learning are statistical analysis techniques that use strategies of artificial intelligence to characterize complex data and extract deep information. In recent years, these techniques have begun to be used not only in traditional computer science test cases, but also in real world applications as well as, more recently, in areas of advanced physics. In condensed matter systems, such techniques have been shown to give useful insight into Ising and spin ice models [1], low dimensional topological systems [2], strongly correlated systems [3], as well as random two- and three-dimensional topological and non-topological systems [4]. It is probably fair to say that machine and deep learning will become standard statistical analysis tools in the future [5].
In this short course, we want to understand some of the basic principles underlying the recent successes of the DL approach to data analysis. We will do so in a hands-on manner, following up our theoretical understanding with examples using state-of-the-art DL packages such as Keras, FastAI, TensorFlow and PyTorch. In doing so, we will use Jupyter notebooks as frontend to Python. Participants are assumed to have their own DL platform at hand (see here for how I installed mine), although mostly modern laptop resources should be sufficient.
[1] J. Carrasquilla and R. G. Melko: Nature Physics 13 (2017) 431; A. Tanaka and A. Tomiya: Journal of the Physical Society of Japan 86 (2017) 063001.
[2] Y. Zhang and E.-A. Kim: Phys. Rev. Lett. 118 (2017) 216401; P. Zhang, H. Shen, and H. Zhai: arXiv:1708.09401 (2017).
[3] G. Carleo and M. Troyer: Science 355 (2017) 602; P. Broecker, J. Carrasquilla, R. G. Melko, and S. Trebst: Scientific Reports 7 (2017) 8823; K. Ch’ng, J. Carrasquilla, R. G. Melko, and E. Khatami: Phys. Rev. X 7 (2017) 031038; L. Li, T. E. Baker, S. R. White, and K. Burke: Phys. Rev. B 94 (2016) 245129; E. P. van Nieuwenburg, Y.-H. Liu, and S. D. Huber: Nature Physics 13 (2017) 435; L. Huang and L. Wang: Phys. Rev. B 95 (2017) 035105; F. Schindler, N. Regnault, and T. Neupert: Phys. Rev. B 95 (2017) 45134; H. Saito: Journal of the Physical Society of Japan 86 (2017) 093001; H. Saito and M. Kato: arXiv:1709.05468 (2017); H. Fujita, Y. Nakagawa, S. Sugiura, and M. Oshikawa: arXiv:1705.05372 (2017).
[4] T. Ohtsuki and T. Ohtsuki: Journal of the Physical Society of Japan 85 (2016) 123706; T. Ohtsuki and T. Ohtsuki: Journal of the Physical Society of Japan 86 (2017) 044708. N. Yoshioka, Y. Akagi, and H. Katsura: arXiv:1709.05790 (2017)
[5] Machine Learning Physics. in J. Phys. Soc. Jpn., Special Topics (ed. Ohtsuki, T.) (Physical Society of Japan, 2024).
Lecture Recordings
nothing yet of course!
Further literature
Please have a look at the .bib file that I use for the lecture notes. It contains many more entries than those actually cited in the lecture notes.
Assessments
Upload hereLink opens in a new window before April 5th, 2026 at 23:59pm!
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