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DTSTART;TZID=America/New_York:20201113T130000
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DTSTAMP:20201021T012439Z
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UID:6473-1605272400-1605283200@it.rutgers.edu
SUMMARY:Machine Learning Series — Random Forest Workshop
DESCRIPTION:[et_pb_section fb_built=”1″ _builder_version=”4.5.7″ _module_preset=”default” custom_padding=”0px||0px|||”][et_pb_row _builder_version=”4.5.7″ _module_preset=”default”][et_pb_column type=”4_4″ _builder_version=”4.5.7″ _module_preset=”default”][et_pb_text _builder_version=”4.5.7″ _module_preset=”default”] \nRegister to attend at the bottom of this page. A Webex link will be emailed after filling out the the registration form. \nInstructor: Yun-juan (Janet) Chang \nTopics:\n1. Introduction to Machine Learning (ML) \n\nBig data and Machine Learning\nRelate Machine Learning to other disciplines\nMachine Learning algorithms\nClassification and Regression\n\n2. Understanding Random Forest (RF) \n\nApplications of Random Forests\nWhy Random Forests\nThe Random Forest Algorithm\nFundamental concepts – ML\, RF\n\n3. Implementing Random Forest \n\nFeature Importance and Feature Selection\nDealing with missing data\, and imbalanced data\nBest split of the node–node impurity\n\n\nOver-fitting and underfitting\nThe model performance\nThe model interpretability\n\n4. Lab Exercise \nWe will use health data combined with gene expression data to build random forest models\, predicting output variables. Both classifier and regressor will be addressed. \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-workshop-11-13-20/
CATEGORIES:OARC
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