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Machine (Deep) Learning for physicists 2023-24

Module Convenor: Rudo Roemer (U Warwick) and Mats Granath (U Gothenburg)

  • Times: Mondays 16-17 UK/17-18 EU and Wednesdays 16-17 UK/17-18 EU
  • 1st (online) lecture: Wednesday, January 10th, 2024
  • Course duration: MPAGS + CY Cergy-Paris Universite, 5 weeks lectures, 5 weeks project work

Join Zoom MeetingLink opens in a new window (Meeting ID: 845 6441 1844, Passcode: 1235813), Slack discussion roomLink opens in a new window

Lecture slidesLink opens in a new window, MatsGranathLectureNotesLink opens in a new window, Warwick Data RepositoryLink opens in a new window, Google ColaboratoryLink opens in a new window

The course is for interested students of the Midlands Physics Alliance Graduate School and the post-graduate students of the EUtopia Alliance (such as those on the CY Cergy-Paris Universite M2-level Postgraduate course 2023-24).

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)

Lecture Recordings

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, April 5th, 2024 at 23:59pm!

This list is for all those MPAGS assessments so that you know whether Djena has received your upload. Really, it means I had the time to see that the email Djena gets from the files.warwick system has arrived and your submission has been downloaded or indeed already marked. When all is done, it's with MPAGS formally and they might let 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)
Emily Roberts 250324 Image classification of sports using Keras/TF marking
Anwesha Sahu 010424 Classification of telescope images for clear and cloudy sky using Keras/TF marking
Ziou He 020424 Images classification of cats and dogs using FastAI 2 marking
Rhys Jordan 030424 Image classification of stars or galaxies using Keras/TF marking
Philipp Muenzer 040424 VAE training for geometrical shapes using PyTorch marking
Mustaqeem Shiffa 050424 Image classification of gen 1 Pokemon using FastAI marking
Fiona Sawyer 050424 Prediction of temperature for the intergalactic medium at high redshift using Keras/TF marking
Adam Taylor 050424 Image classification for dog breeds using Keras/TF marking
Roshan Chacko 050424 Classification of radar returns from the ionosphere using TF marking