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
• 1^st (online) lecture: Wednesday, January 10^th, 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

• Lecture 1Link opens in a new window, Lecture 2,Link opens in a new window Lecture 3Link opens in a new window, Lecture 4Link opens in a new window, Lecture 5Link opens in a new window, Lecture 6Link opens in a new window, Lecture 7Link opens in a new window, Lecture 8Link opens in a new window, Lecture 9 (with Mats)Link opens in a new window, Lecture 10 (also with Mats)Link opens in a new window, Lecture 11Link opens in a new window (all with "magic = MLC#1235813").

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	passed
Anwesha Sahu	010424	Classification of telescope images for clear and cloudy sky using Keras/TF	passed
Ziou He	020424	Images classification of cats and dogs using FastAI 2	passed
Rhys Jordan	030424	Image classification of stars and galaxies using Keras/TF	passed
Philipp Muenzer	040424	VAE training for geometrical shapes using PyTorch	passed
Mustaqeem Shiffa	050424	Image classification of gen 1 Pokemon using FastAI	passed
Fiona Sawyer	050424	Prediction of temperature for the intergalactic medium at high redshift using Keras/TF	passed
Adam Taylor	050424	Image classification for dog breeds using Keras/TF	passed
Roshan Chacko	050424	Classification of radar returns from the ionosphere using TF	passed
Ben Hopton	070424	Image classification of numbers using a subset of MNIST dataset using FastAI v2	passed
Sofia Alonso	160424	Atom finder algorithm for STM images using Keras/TF	passed

• 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
• Warwick course repositoryLink opens in a new window
• Google ColaboratoryLink opens in a new window
• MyGoogleDriveLink opens in a new window