| title | author | layout |
|---|---|---|
Machine Learning Forum |
Benedikt Hegner |
default |
The Inter-Experimental LHC Machine Learning Working Group IML has been launched recently. The group is focused on the development of modern state-of-the art machine learning methods, techniques and practices for high-energy physics problems. The group provides solutions, software and training beneficial for all LHC experiments as well as a forum where on-going work and relevant issues are discussed by the community.
- Develop & sustain MML4HEP expertise – it is not just about software, but need ML know-how & insights, e.g.
- which algorithms to use for which problem
- how to tune hyperparameters
- how to deal with non-continuous or missing variables
- Troubleshooting, novel applications, data vs. MC,...
- Series of dedicated LHC ML challenges to further strengthen & grow MML-HEP interaction, so we can more effectively collaborate
- Second Machine Learning High Energy Physics Summer School 2016, 20-26 June 2016 at Lund University
- CERN openlab Machine Learning and Data Analytics workshop, 29 April 2016 at CERN
- Heavy Flavour Data Mining workshop, 18-20 February 2016 at the Unveristy of Zurich
- Applying Learning to Experimental Physics at NIPS, 11 December 2015 at Montreal
- Data Science @ LHC 2015 Workshop, 9-13 November 2015 at CERN
- Summer school on Machine Learning in High Energy Physics, 27-30 August 2015 at St Petersburg