Resumen
The fundamental physics research at the frontier accessible by today?s particle accelerators such as the CERN Large Hadron Collider pose unique challenges in terms of complexity and abundance of data to analyse. In this context, it is of paramount importance to develop algorithms capable of dealing with multivariate problems to enhance humans? ability to interpret data and ultimately increase the discovery potential of the experiments. Machine learning techniques therefore assume an increasingly important role in the experiments at the LHC. In this work, we give an overview of the latest developments in this field, with a particular focus on the algorithms developed and used within the CMS Collaboration. The review follows this structure: (1) Introduction presents the CMS Experiment at LHC and the most common methods used in particle physics; (2) Jet Flavour Tagging briefly describes the main algorithms used to reconstruct heavy-flavour jets; (3) Jet Substructure and Deep Tagging focuses on the identification of heavy-particle decay in boosted jets; (4) Analysis Applications gives examples of applying the algorithm in physics analyses; and (5) Conclusions summarises the state-of-the-art and gives indications for future studies.