Deep (Machine) Learning for physicists 2020-21
Module Convenor: Rudo Roemer (Warwick University)
- Times: Mondays 15-16 UK/16-17 EU and Wednesdays 15-16 UK/16-17 EU
- 1st (online) lecture: January 11th, 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 , low dimensional topological systems , strongly correlated systems , as well as random two- and three-dimensional topological and non-topological systems . 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.
 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.
 Y. Zhang and E.-A. Kim: Phys. Rev. Lett. 118 (2017) 216401; P. Zhang, H. Shen, and H. Zhai: arXiv:1708.09401 (2017).
 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).
 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