Marín Benito, CarlaCosta Ledesma, Vanessa2022-09-022022-09-022022-06https://hdl.handle.net/2445/188641Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2022, Tutora: Carla Marín BenitoMachine learning algorithms have gained traction in a variety of fields throughout the last decade. This final degree project focuses on a bank problem and on a high-energy physics problem: searching for a rare Λ0b decay. Two different machine learning methods are used: Neural Networks and Boosted Trees, implemented in three different Phython libraries: TensorFlow and Keras, PyTorch and XGBoost. Using the AUC-ROC curve, the models between the three libraries are compared, and finally, models try to predict whether the Λ0b decay happens for a given data. Results for the bank problem shows nearly the same performance for TensorFlow and PyTorch, while XGBoost seems significantly better. For the high-energy problem XGBoost seems better, followed by TensorFlow and last PyTorch. However, predictions made on new data shows similar performance for XGBoost and PyTorch.6 p.application/pdfengcc-by-nc-nd (c) Costa, 2022http://creativecommons.org/licenses/by-nc-nd/3.0/es/Aprenentatge automàticXarxes neuronals (Informàtica)Treballs de fi de grauMachine learningNeural networks (Computer science)Bachelor's thesesinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/openAccess