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X-ORIGINAL-URL:https://oarc.rutgers.edu
X-WR-CALDESC:Events for Office of Advanced Research Computing
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DTSTART:20220313T070000
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DTSTART;TZID=America/New_York:20221202T140000
DTEND;TZID=America/New_York:20221202T160000
DTSTAMP:20260421T150706
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UID:7455-1669989600-1669996800@oarc.rutgers.edu
SUMMARY:Machine Learning with Python and Scikit - Part 1
DESCRIPTION:Machine learning (ML) methods are widely adopted by the academia and industry for applications in science\, engineering\, healthcare\, and humanities. One of the greatest advantages of ML is that they are pretty general and applicable for predictive analytics in many fields. For example\, if someone learned how to apply ML methods to classify animal images\, they can apply the same set of protocols in classifying birds or cars or motorboats. To apply ML for a specific problem\, the practitioners don’t have to go through all the complex mathematics or a lot of statistics but they need to learn the best practices to train and validate the models. \nIn this workshop\, after a brief overview\, we will focus on doing hands-on training in applying ML models on various data types including image\, text\, and time series. We will work through the use cases of classification and regression problems and discuss where to apply supervised or unsupervised methods. \nObjectives of the workshop \n\nUnderstand supervised and unsupervised methods\nDefine metrics for classification vs regression\nFind out which features are important in a given dataset\nLearn to apply ML models such as Decision Trees\, Random Forest\, and Support Vector Machines\nPerform clustering and dimensionality reductions (PCA\, t-sne\, K-means\, etc.)\nSearch the parameter space – hyperparameter optimization\n\nIf you have questions or need help\, please email Bala Desinghu. \nAmarel account: Apply here as soon as possible. You must have an Amarel account set up before the workshop. \nVPN setup: You have to be connected on Rutgers’ network or be on VPN to access Amarel resources. \nSSH setup: Windows users must install an SSH client like PuTTY or MobaXterm. Alternatively\, Windows 10 users can install the complete Windows Subsystem for Linux.
URL:https://oarc.rutgers.edu/event/machine-learning-with-python-and-scikit-12-2-22/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221209T140000
DTEND;TZID=America/New_York:20221209T160000
DTSTAMP:20260421T150706
CREATED:20220809T202902Z
LAST-MODIFIED:20220809T202910Z
UID:7460-1670594400-1670601600@oarc.rutgers.edu
SUMMARY:Machine Learning with Python and Scikit - Part 2
DESCRIPTION:Register to attend this workshop at the bottom of this page. Zoom link will be emailed after filling out the registration form. \nMachine learning (ML) methods are widely adopted by the academia and industry for applications in science\, engineering\, healthcare\, and humanities. One of the greatest advantages of ML is that they are pretty general and applicable for predictive analytics in many fields. For example\, if someone learned how to apply ML methods to classify animal images\, they can apply the same set of protocols in classifying birds or cars or motorboats. To apply ML for a specific problem\, the practitioners don’t have to go through all the complex mathematics or a lot of statistics but they need to learn the best practices to train and validate the models. \nIn this workshop\, after a brief overview\, we will focus on doing hands-on training in applying ML models on various data types including image\, text\, and time series. We will work through the use cases of classification and regression problems and discuss where to apply supervised or unsupervised methods. \nObjectives of the workshop \n\nUnderstand supervised and unsupervised methods\nDefine metrics for classification vs regression\nFind out which features are important in a given dataset\nLearn to apply ML models such as Decision Trees\, Random Forest\, and Support Vector Machines\nPerform clustering and dimensionality reductions (PCA\, t-sne\, K-means\, etc.)\nSearch the parameter space – hyperparameter optimization\n\nIf you have questions or need help\, please email Bala Desinghu. \nAmarel account: Apply here as soon as possible. You must have an Amarel account set up before the workshop. \nVPN setup: You have to be connected on Rutgers’ network or be on VPN to access Amarel resources. \nSSH setup: Windows users must install an SSH client like PuTTY or MobaXterm. Alternatively\, Windows 10 users can install the complete Windows Subsystem for Linux. \nRegister to attend the workshop:\n	Notice: JavaScript is required for this content.
URL:https://oarc.rutgers.edu/event/machine-learning-with-python-and-scikit-12-9-22/
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DTSTART;TZID=America/New_York:20221220T080000
DTEND;TZID=America/New_York:20221221T190000
DTSTAMP:20260421T150707
CREATED:20220105T220510Z
LAST-MODIFIED:20220105T220612Z
UID:6352-1671523200-1671649200@oarc.rutgers.edu
SUMMARY:Amarel monthly maintenance
DESCRIPTION:Each maintenance period is expected to begin at about 08:00 ET on the first day and end by about 19:00 ET on the 2nd day. \n2022 anticipated maintenance schedule (all dates subject to change):\nJAN 11 & 12 \nFEB 15 & 16\nMAR 15 & 16\nAPR 19 & 20\nMAY 17 & 18\nJUN 21 & 22\nJUL 19 & 20\nAUG 16 & 17\nSEP 20 & 21\nOCT 18 & 19\nNOV 15 & 16\nDEC 20 & 21 \nMaintenance is usually scheduled for weekdays because it often involves coordination with University facilities teams or external vendors. There may also be times when Amarel must be unavailable due to externally-scheduled data center facilities\, power\, or telecommunications maintenance. When this happens\, we will do our best to avoid adding to a month’s downtime with our own maintenance work (e.g.\, by working in parallel or postponing some tasks). \n			\n				Visit Amarel system status for more information
URL:https://oarc.rutgers.edu/event/amarel-monthly-maintenance-12-20-22/
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