BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IVADO - ECPv6.5.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:IVADO
X-ORIGINAL-URL:https://vieux.ivado.ca/en/
X-WR-CALDESC:Events for IVADO
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20160313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20161106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20160226T140000
DTEND;TZID=America/New_York:20160226T150000
DTSTAMP:20260422T103748
CREATED:20160208T173851Z
LAST-MODIFIED:20160208T174944Z
UID:5174-1456495200-1456498800@vieux.ivado.ca
SUMMARY:Opportunities and frontiers in machine learning for applied sciences
DESCRIPTION:Presentation of 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. \nGERAD seminar\nFebruary 26t\,h 2016 2:00PM – 3:00PM \nRoom 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/en/events/opportunities-and-frontiers-in-machine-learning-for-applied-sciences/
LOCATION:Université de Montréal\, Pavillon André-Aisenstadt\, Pavillon André-Aisenstadt\, Montréal\, QC\, H3T 1J4\, Canada
END:VEVENT
END:VCALENDAR