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X-WR-CALDESC:Events for Office of Advanced Research Computing
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DTSTART:20230312T070000
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DTSTART:20231105T060000
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DTSTART;TZID=America/New_York:20230707T130000
DTEND;TZID=America/New_York:20230707T160000
DTSTAMP:20260421T005334
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LAST-MODIFIED:20230124T210556Z
UID:8251-1688734800-1688745600@oarc.rutgers.edu
SUMMARY:Machine Learning with Python and Scikit
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. \n\nRegister to attend the workshop:\n\n\n	Notice: JavaScript is required for this content.
URL:https://oarc.rutgers.edu/event/machine-learning-with-python-and-scikit-7-7-23/
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DTSTART;TZID=America/New_York:20230718T080000
DTEND;TZID=America/New_York:20230719T190000
DTSTAMP:20260421T005334
CREATED:20230217T181939Z
LAST-MODIFIED:20230217T200841Z
UID:8438-1689667200-1689793200@oarc.rutgers.edu
SUMMARY:Amarel monthly maintenance
DESCRIPTION:Usually scheduled for every 3rd Tuesday and Wednesday of the month from 08:00 ET on the first day till 19:00 ET on the second day. \n2023 anticipated maintenance schedule (all dates subject to change):\nJAN 24-25 (1 week late)\nFEB 21-22\nMAR 14 -15 (1 week earlier\, spring break)\nAPR 18-19\nMAY 16-17\nJUN 20-21\nJUL 18-19\nAUG 15-16\nSEP 19-20\nOCT 17-18\nNOV 21-22\nDEC 19-20 \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-7-18-23/
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