Machine Learning Approaches in Climate Science

February 25, 2:00pm - 4:00pm
Mānoa Campus, In Person at 103 Keller Hall, 鶹ýManoa or Online via Zoom

Workshop Description

The 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:

Part 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.

Part 2: Using Time Series Prediction on Geospatial Data. We will forecast SST on a global scale from climate simulation data.

Prerequisites

  • Familiarity with python is recommended
Learning Outcomes:

By the end of this workshop attendees will be able to:
  • Apply machine learning methods to time-series and geospatial data.
  • Understand important considerations when modeling climate data.
  • Familiarity with machine learning software tools: scikit learn, matplotlib, and keras.
Tools used in this workshop
  • Google Colab
  • jupyter notebooks
  • scikit learn
  • matplotlib
  • keras


Ticket Information
http://go.hawaii.edu/GsV

Event Sponsor
Hawai‘i Data Science Institute, Mānoa Campus

More Information
956-3503,

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