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Department of Physics

Machine (Deep) Learning for physicists 2021-22

Module Convenor: Rudo Roemer (Warwick University)

  • Times: Mondays 16-17 UK/17-18 EU and Wednesdays 16-17 UK/17-18 EU
  • 1st (online) lecture: Monday, January 17th, 2022
  • Course duration: MPAGS + CY Cergy-Paris Universite, 5 weeks lectures, 5 weeks project work

Join Zoom Meeting (Meeting ID: 944 9524 2929, Passcode: B59b2B), Slack discussion room

Lecture slides, Warwick course repository, Google Colaboratory

The course is for interested students of the Midlands Physics Alliance Graduate School and the CY Cergy-Paris Universite M2-level Postgraduate course 2021-22.

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 may become standard statistical analysis tools in the future.

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)

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 (for MPAGS only)

Upload hereLink opens in a new window before Friday, March 25th, 2022 at 23:59pm!

This list is for all those MPAGS assessments so that you know whether I have received your upload. Really, it means I had the time to see that an email I get from the files.warwick system has arrived and that I have downloaded it or indeed already marked. When all is done, it's with MPAGS formally and they might led you know whether you have pass/fail-ed.

name date received topic as far as we understand it and can summarize it in one sentence here ;-) status (email/download/marking/done)
David Johnson 070322 FastAI prediction of the parameters of a solenoid, only from the magnetic field generated by it, given as an input passed
Sara Calzolari 110322 Image recognition/classification of MRI scans to identify patient with dementia using FastAI passed
Daniel Cornwell 160322 Recognition/classification of galaxy using FastAI passed
Claire Fletcher 170322 Image recognition/classification of rashes, and image recognition/classification of frogs/toads using FastAI passed
Jordan Deville 180322 Image recognition/classification of flowers using FastAI passed
Davide Aloi 230322 Image recognition/classification of MRI scans to identify tumors using FastAI passed
Enrico Martello 230322 Image classification of whale according their dorsal fins using FastAI passed
Bradley March 240322 FastAI code to identify words that rhymes passed
Jacob Young 240322 Corrections of gaussian waveforms alignment using Keras/TF passed
Dennis Lindebaum 250322 Image classification of neutrino events in LArTPC using Keras/TF passed
Katharina Lachner 250322 Image classification of signal vs. background events obtained by the Hyper-Kamiokande observatory using FastAI passed

[table last updated: 060421]