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DTSTART;TZID=America/New_York:20160226T140000
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SUMMARY:Opportunités et limites de l'apprentissage automatique (machine-learning) pour les sciences appliquées (en anglais)
DESCRIPTION:Présentation de Mohammad Attarian Shandiz – Université McGill\, Canada \nModern methods in machine learning have provided many opportunities for solving complex problems in applied science. Hence\, for a data scientist is essential to be familiar with the most important and current fields of research in machine learning and data mining. In this talk\, the most significant fields of research in machine learning and data mining are introduced based on the survey in the database of scientific journals. Subsequently\, various applications of machine learning for some challenging problems in medicine\, finance and engineering are discussed. Lastly\, the results of optimized classifiers for a classification problem in the field of lithium-ion batteries are presented. Ensemble methods including random forests and extremely randomized trees provided the highest accuracy of prediction among other methods for the classification based on the Monte Carlo cross validation tests. \nSéminaire du GERAD\n26 FÉV. 2016 14H00 – 15H00 \nSalle 4488\nPavillon André-Aisenstadt\nCampus de l’Université de Montréal\n 2920\, chemin de la Tour Montréal QC H3T 1J4 Canada
URL:https://vieux.ivado.ca/evenement/opportunities-and-frontiers-in-machine-learning-for-applied-sciences/
LOCATION:Université de Montréal\, Pavillon André-Aisenstadt\, Pavillon André-Aisenstadt\, Montréal\, QC\, H3T 1J4\, Canada
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