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PRODID:-//University of Hawaii//UH Events Calendar//EN
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TZID:Pacific/Honolulu
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CLASS:PUBLIC
CREATED:20260617T132514Z
DESCRIPTION:Workshop Description\n\nThe goals of this lesson are to introduce you to the basics of time-series and geospatial data modeling using modern data science software tools: Jupyter notebooks, ScikitLearn, Keras, and Tensorflow on High Processing Computers. We're approaching this lesson in two parts:\n\nPart 1: Simple Time Series Prediction Using Long-Short-Term-Memory Techniques. We will use time-series of sea surface temperatures (SST) from NOAA buoy data.\n\nPart 2: Using Time Series Prediction on Geospatial Data. We will forecast SST on a global scale from climate simulation data.\n\nPrerequisites\n\nFamiliarity with python is recommended\n\nLearning Outcomes:\n\nBy the end of this workshop attendees will be able to:\n\nApply machine learning methods to time-series and geospatial data.\nUnderstand important considerations when modeling climate data.\nFamiliarity with machine learning software tools: scikit learn, matplotlib, and keras.\n\n\nTools used in this workshop\n\nGoogle Colab\njupyter notebooks\nscikit learn\nmatplotlib\nkeras\n\n
DTEND;TZID=Pacific/Honolulu:20220226T020000Z
DTSTAMP:20260617T132514Z
DTSTART;TZID=Pacific/Honolulu:20220226T000000Z
LAST-MODIFIED:20260617T132514Z
LOCATION:In Person at 103 Keller Hall, UH Manoa or Online via Zoom
PRIORITY:5
SEQUENCE:0
SUMMARY;LANGUAGE=en-us:Machine Learning Approaches in Climate Science
TRANSP:OPAQUE
UID:178173871439680web-support-l@lists.hawaii.edu
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