9-13 November 2015
CERN
Europe/Zurich timezone
There is a live webcast for this event.

Reading materials

Reading materials DS@LHC 2015

 

This documents gather a non-exhaustive list of reading materials targeted i) for physicists with sparse knowledge of machine learning and ii) for machine learning experts interested in applications of their methods to high energy physics problems.

 

Please contact us if you wish to add links to this list.

Machine learning for physicists

 

Supervised learning:

 

Deep learning:

 

Gaussian Processes:

  • Rasmussen, Carl Edward. "Gaussian processes for machine learning." (2006).

 

Statistical inference:

  • Gelman, Andrew, et al. “Bayesian data analysis”. Vol. 2. London: Chapman & Hall/CRC, 2014.

  • Cranmer, Kyle. "Approximating Likelihood Ratios with Calibrated Discriminative Classifiers." arXiv preprint arXiv:1506.02169 (2015). http://arxiv.org/abs/1506.02169

High energy physics for data scientists

 

 

Examples of machine learning in high energy physics:

  • Roe, Byron P., et al. "Boosted decision trees as an alternative to artificial neural networks for particle identification." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 543.2 (2005): 577-584. http://arxiv.org/abs/physics/0408124

  • Yang, Hai-Jun, Byron P. Roe, and Ji Zhu. "Studies of boosted decision trees for MiniBooNE particle identification." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 555.1 (2005): 370-385. http://arxiv.org/abs/physics/0508045

Software

General purpose libraries for ML:

 

Deep Learning:

 

Gaussian processes:

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