ROOT provides advanced statistical methods needed by the LHC experiments to analyze their data. These include machine learning tools for classification, regression and clustering. TMVA, a toolkit for multi-variate analysis in ROOT, provides these machine learning methods.
We will present new developments in TMVA, including parallelisation, deep-learning neural networks, new features and
additional interfaces to external machine learning packages.
We will show the new modular design of the new version of TMVA, cross-validation and hyper parameter tuning capabilities, feature engineering and deep learning.
We will further describe new parallelisation features including multi-threading, multi-processing, cluster parallelisation and present GPU support for intensive machine learning applications, such as deep learning.
|Primary Keyword (Mandatory)||Artificial intelligence/Machine learning|
|Secondary Keyword (Optional)||Algorithms|
|Tertiary Keyword (Optional)||Analysi tools and techniques|