  This report entails the detailed description of the approach and
methodologies taken as part of competing in the Higgs Boson Machine Learning
Competition hosted by Kaggle Inc. and organized by CERN et al. It briefly
describes the theoretical background of the problem and the motivation for
taking part in the competition. Furthermore, the various machine learning
models and algorithms analyzed and implemented during the 4 month period of
participation are discussed and compared. Special attention is paid to the Deep
Learning techniques and architectures implemented from scratch using Python and
NumPy for this competition.
