Machine Learning Approaches in Climate Science 🗓

Registration: http://go.hawaii.edu/GsV

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
Scheduled events

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