Machine learning at CERN: ATLAS, LHCb, and more

Machine learning is of increasing importance to high energy physics as dataset sizes and data rates grow, while sensitivity to standard model and new physics signals are continually pushed to new extremes. Machine learning has proven to be advantageous in many contexts, and applications now span areas as diverse as triggering, monitoring, reconstruction, simulation, and data analysis. This talk will discuss a subset of the applications of machine learning in the ATLAS and LHCb experiments, as well as other areas of more general use in high energy physics at CERN.