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DTSTART:20190101T000000
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DTSTART;TZID=UTC:20200928T130000
DTEND;TZID=UTC:20200928T153000
DTSTAMP:20200904T195212Z
CREATED:20200902T003819Z
LAST-MODIFIED:20200904T195212Z
UID:5559-1601298000-1601307000@it.rutgers.edu
SUMMARY:Machine Learning Series — Random Forest
DESCRIPTION:[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”3.22″ custom_padding=”0px||0px|||”][et_pb_row admin_label=”row” _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_padding=”0px||0px|||”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text admin_label=”Text” _builder_version=”4.5.7″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”] \nRegister to attend at the bottom of this page. A Webex link will be emailed after filling out the the registration form. \nTopics: \n\nIntroduction to Machine Learning (ML)\n\nBig data and Machine Learning\nRelate Machine Learning to other disciplines\nMachine Learning algorithms\nClassification and Regression\n\n\nRandom Forest (RF)\n\nApplications of Random Forest\nWhy Random Forest\nUnderstanding Random Forest\nFundamental concepts – ML\nFundamental concepts — RF\n\n\nRandom Forest – How\n\nHow to select relevant features — Feature Selection.\nHow to deal with missing data – Proximity Matrix\nHow to split the node –node impurity\nHow to limit over-fitting\nHow to evaluate the model performance &\n\n\nLab Exercise:\n\nFeature selection and evaluation\nRandom Forest Classification\nRandom Forest Regression\n\n\n\nRegister: \n\n	Notice: JavaScript is required for this content.\nNo Fields Found.\n\n    \n\n\n        \n        \n         \n[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section]
URL:https://it.rutgers.edu/event/machine-learning-series-random-forest-9-28-20/
CATEGORIES:OARC
ORGANIZER;CN="OARC":MAILTO:yc759@oarc.rutgers.edu
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