For the organizers of the Summer School on Machine Learning for High Energy Physics 2016:
This is the first announcement of the Summer School on Machine Learning for High Energy Physics 2016, to be held at Lund, Sweden, June 20-26, 2016, as a satellite event of LHC Physics Conference: http://bit.ly/mlhep2016.
The primary goal of the MLHEP school will be a focused introduction to modern machine learning techniques that could improve physics performance for variety of HEP-related problems. Along with “hands-on” seminars, a dedicated data science competition will be organized. The school will include a series of talks that show real examples of improvements for particular physics cases using machine learning techniques. The school is suited for advanced graduate students and young postdocs willing to learn how to
– formulate HEP-related problem in ML-friendly terms
– select quality criteria for given problems
– understand and apply principles of widely used classification models (e.g. boosting, bagging, BDT, neural networks, etc) to practical cases
– optimize features and parameters of given models efficiently under given restrictions
– select the best classifier implementation amongst variety of ML libraries (scikit-learn, xgboost, deep learning libraries, etc)
– define and conduct reproducible data-driven experiments
For further information, including registration procedures, please refer to the summer school website: http://bit.ly/mlhep2016. Early registration deadline is April 30, 2016.