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DTSTART:20160313T070000
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DTSTART;TZID=America/New_York:20160219T104500
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DTSTAMP:20260423T151219
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SUMMARY:Optimiser les infrastructures du Cloud de Google (en anglais)
DESCRIPTION:Conférence de Brian Eck – Google\, États-Unis \nGoogle’s Cloud infrastructure consists of the servers\, network\, datacenters and software that run all of Google’s services like Search\, Gmail and Youtube as well as it’s fast growing external business\, Google Cloud Platform. We’ll discuss three case studies in optimizing this infrastructure\, covering challenges not only in solving the analytical problems but also in solving the business problems to reach real implementation and impact. Case studies will include some subset of: \n\nOptimizing build frequency for adding capacity to the network\,\nAnalyzing scenarios of metro network topology to inform strategy\, including cost implications of adjusting forecasts\,\nBuilding ‘forecast tolerant’ networks that buffer for fault-tolerance and forecast variation\,\nLow-cost methods of physical grooming on a city-level fiber infrastructure\,\nReducing stranded compute resources through simulation\,\nIncreasing resource utilization through statistical oversubscription.\n\nSéminaire du GERAD\nLe 19 Février 2016 de 10H45 – 12H00 \nSalle 4488\nPavillon André-Aisenstadt\nCampus de l’Université de Montréal\n2920\, chemin de la Tour Montréal QC H3T 1J4 Canada
URL:https://vieux.ivado.ca/evenement/optimiser-les-infrastructures-du-cloud-de-google-en-anglais/
LOCATION:Université de Montréal\, Pavillon André-Aisenstadt\, Pavillon André-Aisenstadt\, Montréal\, QC\, H3T 1J4\, Canada
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DTSTART;TZID=America/New_York:20160226T140000
DTEND;TZID=America/New_York:20160226T150000
DTSTAMP:20260423T151219
CREATED:20160208T173851Z
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UID:5172-1456495200-1456498800@vieux.ivado.ca
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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