Module Convenor: Rudo Roemer (Warwick University)

• Times: Mondays 15-16 UK/16-17 EU and Wednesdays 15-16 UK/16-17 EU
• 1^st (online) lecture: January 11^th, 2021
• Course duration: MPAGS 5 weeks, CY Cergy-Paris Universite 5+5 weeks (including course 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.

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, although mostly modern laptop resources will 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)

Assessments (for MPAGS only)

Upload here! 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. The done stage will not be reached until the deadline of March 19th has been passed, i.e. you have 5 weeks to work on this.

name	date received	topic as far as I understand it and can summarize it in one sentence here ;-)	status (email/download/marking/done)
Joel Swallow	120221	Simulations of K+ decays at the NA62 experiment at CERN using FastAI	passed
Joe Kelly	120221	B/W/images of human faces compressed for efficient storage and reconstructed using variational autoencoder [TF, Keras]	passed
Billy Shrive	160221	Solely numpy based deep learning code from scratch	passed
Matthew Nicholson	190221	Tests how well neural networks can cope if they have a particularly limited dataset, i.e. pictures of Matthew's two dogs. Answer: 500 images are not enough [PyTorch]	passed
Chris Oliver	190221	classification of X-ray images of human bones into normal/abnormal using FastAI	passed
Alun Rees	050321	scikit-learn, TF and Keras based prediction of NRL games	passed
Clar-Brid Tohill	080321	keras+TF to distinguish healthy from disease-afflicted plants via photos of their leaves	passed
Wanbing Ge	090321	PyTorch version of image recognition/classification using two different DL architectures	passed
Daniel Cocking	100321	FastAI version of image recognition/classification for wild cats (lion, tiger, etc.)	passed
Jack Henshaw	120321	FastAI version of image recognition/classification for bees, waps and other insects or other non-insects	passed
Vilius Atkocius	150321	FastAI SuperRes-GAN based method to reconstruct background images from experimentally determined atomic densities profiles	passed
Tishtrya Mehta	160321	FastAI version of image recognition/classification for more than 200 types of birds	passed
Daniele Baldolini	170321	FastAI version of image recognition/classification for pictures of chess pieces as published in news paper chess columns, great increase in accuracy following data augmentation	passed
Luke Smith	180321	FastAI code to distinguish birds from planes	passed
Elizabeth Sharpe	190321	FastAI code to distinguish three macaw species	passed
Gethin-Wyn Williams	190321	A FastAI tabular application training in the Titanic dataset from Kaggle	passed
Lucas Rushton	190321	Crack and defect detection using Keras	passed
Olly Smith	190321	FastAI code to learn reading images of road signs	passed
Mihaela Marinescu	190321	FastAI code distinguish images of chihuahua dogs from those of muffins. Checkout this Chihuahua or muffin twitter post to see this is a non-trivial image recognition task - although very funny ;-)	passed
Sebastian Coleman	190321	FastAI image recognition to distinguish three popular types of coffee.	passed
Andreas Hadjigeorghiou	190321	Training of FastAI model for classifying major members of the canine family, including: wolves, jackals, foxes, african wild dogs and siberian huskies	passed

[table last updated: 060421]

• Join Zoom Meeting
  (Meeting ID: 944 9524 2929, Passcode: B59b2B)
• Slack discussion room
• Lecture slides
• Warwick course repository
• Google Colaboratory