Technical Report of Participation in Higgs Boson Machine Learning Challenge

TL;DR

This report details the methodologies and machine learning techniques, including deep learning architectures built from scratch, used in the Higgs Boson Machine Learning Challenge organized by CERN and Kaggle.

Contribution

It provides a comprehensive description of the approaches, models, and deep learning architectures developed and tested during the four-month competition period.

Findings

Comparison of different machine learning models

Implementation of deep learning architectures from scratch

Analysis of model performance and effectiveness

Abstract

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.