Machine Learning Approaches in Climate ScienceFebruary 25, 2:00pm - 4:00pm
Mānoa Campus, In Person at 103 Keller Hall, UH Manoa 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
- 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.
- Google Colab
- jupyter notebooks
- scikit learn
Hawai‘i Data Science Institute, Mānoa Campus
Friday, February 25
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Machine Learning Approaches in Climate ScienceMānoa Campus, In Person at 103 Keller Hall, UH Manoa or Online via Zoom
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