{"id":13870170,"url":"https://github.com/iml-wg/HEP-ML-Resources","last_synced_at":"2025-07-15T20:31:39.078Z","repository":{"id":52208542,"uuid":"89476450","full_name":"iml-wg/HEP-ML-Resources","owner":"iml-wg","description":"Listing of useful learning resources for machine learning applications in high energy physics (HEPML)","archived":false,"fork":false,"pushed_at":"2021-05-05T05:47:36.000Z","size":260,"stargazers_count":329,"open_issues_count":7,"forks_count":117,"subscribers_count":48,"default_branch":"master","last_synced_at":"2024-08-06T21:23:30.764Z","etag":null,"topics":["hep","machine-learning"],"latest_commit_sha":null,"homepage":null,"language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/iml-wg.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-04-26T12:07:33.000Z","updated_at":"2024-08-06T09:10:26.000Z","dependencies_parsed_at":"2022-09-11T02:01:58.796Z","dependency_job_id":null,"html_url":"https://github.com/iml-wg/HEP-ML-Resources","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iml-wg%2FHEP-ML-Resources","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iml-wg%2FHEP-ML-Resources/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iml-wg%2FHEP-ML-Resources/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iml-wg%2FHEP-ML-Resources/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/iml-wg","download_url":"https://codeload.github.com/iml-wg/HEP-ML-Resources/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":226068265,"owners_count":17568729,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["hep","machine-learning"],"created_at":"2024-08-05T20:01:32.638Z","updated_at":"2024-11-23T16:31:16.416Z","avatar_url":"https://github.com/iml-wg.png","language":"TeX","funding_links":[],"categories":["TeX"],"sub_categories":[],"readme":"# HEPML Resources\n\n[![DOI](https://zenodo.org/badge/89476450.svg)](https://zenodo.org/badge/latestdoi/89476450)\n[![license](https://img.shields.io/github/license/iml-wg/HEP-ML-Resources.svg)](https://opensource.org/licenses/MIT)\n\nListing of useful (mostly) public learning resources for machine learning applications in high energy physics (HEPML). Listings will be in reverse chronological order (like a CV).\n\n\u003e **N.B.:** This listing will almost certainly be biased towards work done by ATLAS scientists, as the maintainer is a member of ATLAS and so sees ATLAS work the most. However, this is not the desired case and [help to diversify this listing](#contributing) would be greatly appreciated.\n\n## Table of contents\n\n- [Introductory Material](#introductory-material)\n   - [Lectures](#lectures)\n   - [Seminar Series](#seminar-series)\n   - [Tutorials](#tutorials)\n   - [Schools](#schools)\n   - [Courses](#courses)\n   - [Journals](#journals)\n- [Software](#software)\n- [Public Datasets](#public-datasets)\n- [Papers](#papers)\n- [Workshops](#workshops)\n- [Tweets](#tweets)\n- [Other HEP Resource Collections](#other-hep-resource-collections)\n- [People](#people)\n- [Contributing](#contributing)\n\n## Introductory Material ![Introductory](https://img.shields.io/badge/subject-introductory-blue.svg)\n\n### Lectures\n\n- [Introduction to GANs](https://indico.cern.ch/event/655447/contributions/2742176/), by [Luke de Oliveira](https://ldo.io/) (November 3, 2017)\n\n- [Frontiers with GANs](https://indico.cern.ch/event/655447/contributions/2742180/), by [Michela Paganini](http://mickypaganini.github.io) (November 3, 2017)\n\n- [Nikhef Colloquium: \"Teaching machines to discover particles\"](https://indico.nikhef.nl/event/878/), by [Gilles Louppe](https://glouppe.github.io/) (September 29, 2017)\n\n- [CERN Academic Training Lecture Regular Programme](https://indico.cern.ch/category/72/), April 2017 (Machine Learning):\n\n  - [Machine Learning (Lecture 1)](https://indico.cern.ch/event/619370/) --- [Michael Kagan](https://www.linkedin.com/in/michael-kagan-06292616/) (SLAC)\n  - [Machine Learning (Lecture 2)](https://indico.cern.ch/event/619371/) --- [Michael Kagan](https://www.linkedin.com/in/michael-kagan-06292616/) (SLAC)\n  - [Deep Learning and Vision](https://indico.cern.ch/event/619372/) --- [Jonathon Shlens](https://research.google.com/pubs/JonathonShlens.html) (Google Research)\n\n- [Deep Learning in High Energy Physics](https://youtu.be/cSxQPFb0yOw), by [Amir Farbin](http://www.uta.edu/physics/pages/faculty/profiles/farbin/index.html)\n\n### Seminar Series\n\n- [CERN Data Science Seminars](https://indico.cern.ch/category/9320/)\n\n- [Inter-Experimental LHC Machine Learning Working Group](https://iml.web.cern.ch/) Guest Seminars:\n  - [Open challenges for improving Generative Adversarial Networks (GANs)](https://indico.cern.ch/event/673989/), by [Ian Goodfellow](http://www.iangoodfellow.com/) (October 27, 2017)\n\n### Tutorials\n\n- [PyTorch Deep Learning Minicourse](https://github.com/Atcold/pytorch-Deep-Learning-Minicourse) - [CoDaS-HEP 2018](https://indico.cern.ch/event/707498/timetable/), by [Alfredo Canziani](https://github.com/Atcold) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Atcold/PyTorch-Deep-Learning-Minicourse/master)\n\n- [Intro Tutorial on GANs](https://indico.fnal.gov/event/16720/), by [Michela Paganini](http://mickypaganini.github.io) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mickypaganini/gan_tutorial/master)\n\n- [Scikit-learn Tutorial](https://indico.cern.ch/event/595059/contributions/2522192/), by [Gilles Louppe](https://glouppe.github.io/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/glouppe/tutorials-iml2017/master) [![nbviewer](https://img.shields.io/badge/view%20on-nbviewer-brightgreen.svg)](https://nbviewer.jupyter.org/github/glouppe/tutorials-iml2017/tree/master/)\n\n- [TMVA Tutorial](https://indico.cern.ch/event/595059/contributions/2522191/), by [Lorenzo Moneta](https://phonebook.cern.ch/phonebook/#personDetails/?id=415998)\n\n- [Keras and TMVA interfaces Tutorial](https://indico.cern.ch/event/595059/contributions/2522193/), by [Stefan Wunsch](https://www.ims.kit.edu/14_117.php)\n\n- [Boosted Decision Tree Tutorial (using XGBoost)](https://github.com/k-woodruff/bdt-tutorial), by [Katherine Woodruff](https://tele.fnal.gov/cgi-bin/telephone.script?type=name_last\u0026accuracy=contains\u0026string=WOODRUFF) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/k-woodruff/bdt-tutorial/master) [![nbviewer](https://img.shields.io/badge/view%20on-nbviewer-brightgreen.svg)](http://nbviewer.jupyter.org/github/k-woodruff/bdt-tutorial/tree/master/)\n\n- [Introduction to Deep Learning with Keras Tutorial](https://gist.github.com/lukedeo/0654e7310432d6d435126c556b863907), by [Luke de Oliveira](https://ldo.io/)\n\n- [Introduction to Deep Learning with Keras Tutorial](https://indico.cern.ch/event/487416/contributions/2174907/) - [2nd Developers@CERN Forum](https://indico.cern.ch/event/487416/timetable/), by [Michela Paganini](http://mickypaganini.github.io)\n\n### Schools\n\n#### HEP-ML:\n\n##### Upcoming:\n\n- [Deep Learning for Science Summer School 2019, Berkeley, CA, USA](https://dl4sci-school.lbl.gov/home) (July 15-19, 2019)\n\n##### Past:\n\n- [5th Machine Learning in High Energy Physics Summer School 2019](https://indico.cern.ch/event/768915/) (July 1-10, 2019)\n   - Associated [Yandex School of Data Analysis](https://github.com/yandexdataschool) repo: [mlhep2019](https://github.com/yandexdataschool/mlhep2019)\n- [4th Machine Learning in High Energy Physics Summer School 2018](https://indico.cern.ch/event/687473/) (August 6-12, 2018)\n   - Associated [Yandex School of Data Analysis](https://github.com/yandexdataschool) repo: [mlhep2018](https://github.com/yandexdataschool/mlhep2018)\n- [2nd Computational and Data Science school for High Energy Physics (CoDaS-HEP 2018)](https://indico.cern.ch/event/707498/) (July 23-27, 2018)\n- [3rd Machine Learning in High Energy Physics Summer School 2017](https://indico.cern.ch/event/613571/) (July 17-23, 2017)\n   - Associated [Yandex School of Data Analysis](https://github.com/yandexdataschool) repo: [mlhep2017](https://github.com/yandexdataschool/mlhep2017)\n- [1st Computational and Data Science School for High Energy Physics (CoDaS-HEP)](https://indico.cern.ch/event/625333/) (July 10-13, 2017)\n- [2nd Machine Learning in High Energy Physics Summer School 2016](https://indico.cern.ch/event/497368/overview) (June 20-26, 2016)\n   - Associated [Yandex School of Data Analysis](https://github.com/yandexdataschool) repo: [mlhep2016](https://github.com/yandexdataschool/mlhep2016)\n- [1st Machine Learning in High Energy Physics Summer School 2015](https://www.hse.ru/mlhep2015) (August 27-30, 2015)\n   - Associated [Yandex School of Data Analysis](https://github.com/yandexdataschool) repo: [mlhep2015](https://github.com/yandexdataschool/mlhep2015)\n\n#### Deep Learning:\n\n##### Upcoming:\n\n- [Machine Learning Summer School 2019, London, UK](https://sites.google.com/view/mlss-2019/home?authuser=0) (July 15–26, 2019)\n- [Machine Learning Summer School 2019, Stellenbosch, South Africa](http://mlssafrica.com/) (January 7-18, 2019)\n\n##### Past:\n\n- [Machine Learning Summer School 2018, Madrid, Spain](http://mlss.ii.uam.es/mlss2018/index.html) (August 27 - September 7, 2018)\n- [Machine Learning Summer School 2018, Buenos Aires, Argentina](http://mlss2018.net.ar/) (June 18-20, 2018)\n- [Deep Learning and Reinforcement Learning Summer School, Toronto, Canada](https://dlrlsummerschool.ca/) (July 25 - August 3, 2018)\n- [PAISS: Artificial Intelligence Summer School, Grenoble, France](https://project.inria.fr/paiss/) (July 2-6, 2018)\n- [Deep Learning Summer School 2016](https://sites.google.com/site/deeplearningsummerschool2016/) (August 1-7, 2016)\n\n### Courses\n\n- [Advanced Machine Learning](http://www.montefiore.ulg.ac.be/~geurts/Cours/AML/aml2017_2018.html), Pierre Geurts, [Gilles Louppe](https://glouppe.github.io/), and Louis Wehenkel (Spring, 2018 - Université de Liège, Institut Montefiore)\n\n- [Applications of Deep Learning to High Energy Physics](https://wiki.uta.edu/display/~afarbin/Physics+4%285%29391-002+-+Applications+of+Deep+Learning+to+High+Energy+Physics), [Amir Farbin](http://www.uta.edu/physics/pages/faculty/profiles/farbin/index.html) (Spring, 2017 - University of Texas at Arlington)\n   - Associated GitHub repository: [DSatHEP-Tutorial](https://github.com/UTA-HEP-Computing/DSatHEP-Tutorial)\n\n- [Tensorflow for Deep Learning Research](https://web.stanford.edu/class/cs20si/syllabus.html), (Spring, 2017 - Stanford Univeristy)\n\n- Introduction to Machine Learning and Convolutional Neural Networks for Visual Recognition:\n  - [Spring, 2017](https://www.youtube.com/watch?v=vT1JzLTH4G4\u0026list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) - Stanford University, [Fei-Fei Li](http://vision.stanford.edu/feifeili/), [Justin Johnson](http://cs.stanford.edu/people/jcjohns/), [Serena Yeung](http://ai.stanford.edu/~syyeung/)\n  - [Winter, 2016](https://www.youtube.com/watch?v=NfnWJUyUJYU) - Stanford University, [Andrej Karpathy](https://cs.stanford.edu/people/karpathy/), [Fei-Fei Li](http://vision.stanford.edu/feifeili/), [Justin Johnson](http://cs.stanford.edu/people/jcjohns/)\n\n### Journals\n\n- [Distill Research Journal](http://distill.pub/)\n\n## Software\n\n#### Common software tools and environments used in HEP for ML\n\n- Python environments for scientific computing\n\n    - The [Conda package and environment manager](https://conda.io/docs/) and [Anaconda](https://www.continuum.io/anaconda-overview) Python library collection\n\n      - [Using ROOT/PyROOT with Conda and NumPy](https://indico.cern.ch/event/619371/attachments/1450504/2236434/Kagan_Lecture2.pdf#page=99)\n\n    - [scikit-learn](http://scikit-learn.org/stable/): General machine learning Python library\n\n- [TMVA](https://root.cern.ch/tmva): ROOT's builtin machine learning package\n\n  - [TMVA-branch-adder](https://github.com/pseyfert/tmva-branch-adder): wrapper to add TMVA response to TTree without boiler plate code\n\n### High level deep learning libraries/framework APIs\n\n- [Keras](https://keras.io/)\n\n### Deep learning frameworks\n\n- [TensorFlow](https://www.tensorflow.org/)\n- [Theano](http://deeplearning.net/software/theano/)\n- [PyTorch](http://pytorch.org/)\n- [Caffe2](https://caffe2.ai/)\n- [List of Conversion Tools For Saved Networks](https://github.com/ysh329/deep-learning-model-convertor)\n\n### HEP to ML bridge tools\n\n- [lwtnn](https://github.com/lwtnn/lwtnn): Tool to run Keras networks in C++ code\n\n- [sklearn-porter](https://github.com/nok/sklearn-porter): Transpile trained scikit-learn estimators to C, Java, JavaScript and others\n\n- [ONNX](https://onnx.ai) open format to represent deep learning models\n\n- [Scikit-HEP](http://scikit-hep.org/): Toolset of interfaces and Python tools for Particle Physics\n\n  - [root_numpy](https://github.com/scikit-hep/root_numpy): The interface between ROOT and numpy\n\n  - [root_pandas](https://github.com/scikit-hep/root_pandas): An upgrade of root_numpy to use with pandas\n\n  - [uproot](https://github.com/scikit-hep/uproot): Mimimalist ROOT to numpy converter (no dependency on ROOT)\n\n- [ttree2hdf5](https://github.com/dguest/ttree2hdf5): Mimimalist ROOT to HDF5 converter (written in C++)\n\n- [hep_ml](https://github.com/arogozhnikov/hep_ml): Python algorithms and tools for HEP ML use cases\n\n### Images for Containerized Environments\n\n- [ATLAS Machine Learning Docker images](https://gitlab.cern.ch/aml/containers/docker): Base images for a modern Python 3 machine learning environment for physics\n\n\u003c!-- ## Notebooks - [Vince Croft RooFit Notebooks](https://www.nikhef.nl/~vcroft/notebooks.html) --\u003e\n\n## Public Datasets\n\n- [CERN IML public datasets listing](https://iml.web.cern.ch/public-datasets): Listing of public datsets that are used for machine learning studies at the LHC.\n\n## Papers\n\n\u003e A `.bib` file for all papers listed is [available in the `tex` directory](https://github.com/iml-wg/HEP-ML-Resources/blob/master/tex/HEPML.bib).\n\nA listing of papers of applications of machine learning to high energy physics can be found in [`papers.md`](https://github.com/iml-wg/HEP-ML-Resources/blob/master/papers.md).\n\n## Workshops\n\n### Upcoming\n\n- TBA\n\n### Past\n\n- [Machine Learning for Jet Physics (2020)](https://indico.cern.ch/event/809820/) (January 15-17, 2020)\n- [Machine Learning and the Physical Sciences at NIPS](https://ml4physicalsciences.github.io/) (December 14, 2019)\n- [4th ATLAS Machine Learning Workshop (2019)](https://indico.cern.ch/event/844092/) (November 11-15, 2019)\n- [Fast Machine Learning IRIS-HEP Blueprint Workshop](https://indico.cern.ch/event/822126/) (September 10-13, 2019)\n- [3rd CMS Machine Learning Workshop (2019)](https://indico.cern.ch/event/798721/) (June 17-19, 2019) ![CMS only](https://img.shields.io/badge/restricted-CMS-red.svg)\n- [Theoretical Physics for Deep Learning at ICML 2019](https://sites.google.com/view/icml2019phys4dl) (June 14, 2019)\n- [3rd IML Machine Learning Workshop (2019)](https://indico.cern.ch/event/766872/) (April 15-18, 2019)\n- [Accelerating the Search for Dark Matter with Machine Learning (2019)](http://indico.ictp.it/event/8674/) (April 8-12, 2019)\n- [5th Connecting The Dots / Intelligent Trackers (2019)](https://indico.cern.ch/event/742793/) (April 2-5, 2019)\n- [19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019)](https://indico.cern.ch/event/708041/) (March 11-15, 2019)\n- [Machine Learning for Jet Physics (2018)](https://indico.cern.ch/event/745718/) (November 14-16, 2018)\n- [3rd ATLAS Machine Learning Workshop (2018)](https://indico.cern.ch/event/735932/) (October 15-17, 2018)\n- [2nd CMS Machine Learning Workshop (2018)](https://indico.cern.ch/event/730677/) (July 2-4, 2018) ![CMS only](https://img.shields.io/badge/restricted-CMS-red.svg)\n- [2nd IML Machine Learning Workshop (2018)](https://indico.cern.ch/event/668017/) (April 9-12, 2018)\n- [Machine Learning for Phenomenology (2018)](https://conference.ippp.dur.ac.uk/event/660/) (April 3-6, 2018)\n- [4th International Connecting The Dots Workshop (2018)](https://indico.cern.ch/event/658267/) (March 20-22, 2018)\n- [Accelerating the Search for Dark Matter with Machine Learning (2018)](https://indico.cern.ch/event/664842/) (January 15-19, 2018)\n- [Machine Learning for Jet Physics (2017)](https://indico.physics.lbl.gov/indico/event/546/) (December 11-13, 2017)\n- [Deep Learning for Physical Sciences at NIPS](https://dl4physicalsciences.github.io/) (December 8, 2017)\n- [18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017)](https://indico.cern.ch/event/567550/) (August 21-25, 2017)\n- [Hammers \u0026 Nails - Machine Learning \u0026 HEP](https://www.weizmann.ac.il/conferences/SRitp/Summer2017/) (July 19-28, 2017)\n- [CMS Machine Learning Workshop (2017)](https://indico.cern.ch/event/646801) (July 5-6, 2017) ![CMS only](https://img.shields.io/badge/restricted-CMS-red.svg)\n- [2nd ATLAS Machine Learning Workshop (2017)](https://indico.cern.ch/event/630665/overview) (June 6-9, 2017) ![ATLAS only](https://img.shields.io/badge/restricted-ATLAS-red.svg)\n- [Workshop on Machine Learning and b-tagging](https://indico.cern.ch/event/615994/overview) (May 23-26, 2017) ![ATLAS only](https://img.shields.io/badge/restricted-ATLAS-red.svg)\n- [DS@HEP 2017](https://indico.fnal.gov/conferenceDisplay.py?confId=13497) (May 8-12, 2017)\n- [2nd S2I2 HEP/CS Workshop (Parallel Session)](https://indico.cern.ch/event/622920/timetable/#5-parallel-session-machine-lea) (May 1-3, 2017)\n- [CERN openlab workshop on Machine Learning and Data Analytics](https://indico.cern.ch/event/627852/) (April 27, 2017)\n- [First IML Workshop on Machine Learning](https://indico.cern.ch/event/595059/) (March 20-22, 2017)\n- [DS@HEP at the Simons Foundation](https://indico.hep.caltech.edu/indico/conferenceDisplay.py?confId=102) (July 5-7, 2016)\n- [ALICE Mini-Workshop 2016: Statistical Methods and Machine Learning Tutorial](https://indico.cern.ch/event/514695/) (May 18, 2016) ![ALICE only](https://img.shields.io/badge/restricted-ALICE-red.svg)\n- [ATLAS Machine Learning Workshop (2016)](https://indico.cern.ch/event/483999/) (March 29-31, 2016) ![ATLAS only](https://img.shields.io/badge/restricted-ATLAS-red.svg)\n- [Heavy Flavour Data Mining workshop](https://indico.cern.ch/event/433556/) (February 18-20, 2016)\n- [Data Science @ LHC 2015](https://indico.cern.ch/event/395374/) (November 9-13, 2015)\n\n## Tweets\n\n- [#HEPML collection of tweets](https://twitter.com/search?q=HEPML\u0026src=typd)\n\n## People\n\n- [HEPML directory](http://mickypaganini.github.io/HEPML_directory): Opt-in list of people working at the intersection of Machine Learning and High Energy Physics\n   - Add yourself through the [Google form](https://t.co/jprokVZEiK)\n\n## Other HEP Resource Collections\n\n- [HEP Software Foundation](https://github.com/hsf-training)'s list of [Python Libraries of Interest to Particle Physics](https://github.com/hsf-training/PyHEP-resources)\n\n## Contributing\n\nContributions to help improve the listing are very much welcome! Please read [CONTRIBUTING.md](https://github.com/matthewfeickert/HEP-ML-Resources/blob/master/CONTRIBUTING.md) for details on the process for submitting pull requests or filing issues.\n\n## Authors\n\nListing maintainer: [Matthew Feickert](http://www.matthewfeickert.com/)\n\n## Acknowledgments\n\n- Following [PurpleBooth](https://github.com/PurpleBooth)'s [README style](https://gist.github.com/PurpleBooth/109311bb0361f32d87a2)\n- All badges made by [shields.io](http://shields.io/)\n- Inspiration for this listing came from the [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) repo and [Dustin Tran](http://dustintran.com/)'s [Machine Learning Videos](https://github.com/dustinvtran/ml-videos) repo\n- Many thanks to [everyone who has contributed their time](https://github.com/iml-wg/HEP-ML-Resources/graphs/contributors) to improve this project\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiml-wg%2FHEP-ML-Resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fiml-wg%2FHEP-ML-Resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiml-wg%2FHEP-ML-Resources/lists"}