This workshop is designed to provide overview and hands-on experience for popular tools and methods in various fields of Machine Learning. Physicists are welcome to share challenges they are facing to facilitate collaboration with ML-practitioners. Active Machine Learning practitioners are invited to share their experience and their instruments that they use to achieve meaningful results in their domains of interest. Those fields might include Natural Language Processing, Image Recognition, Robotics. Those areas seemingly being away from HEP still have plenty of tools and algorithms could be applied to HEP challenges as well.
To foster the interaction OpenSpace Technology will be used, that is recognized as an approach for boosting creativity in variety of contexts and that "can lead to surprising results and fascinating new questions".
Winners of the Physics Prize of Flavours of Physics challenge organized by CERN, Yandex at Kaggle will present their solutions. Practical introduction into ML toolkits would be covered by tutorials on scikti-learn (by Gilles Loupe - core developer of scikit-learn), REP, hep_ml, Deep Learning tools.
