Machine Learning in Python for Environmental Science Problems AMS 2020 Short Course https://sites.google.com/rams.colostate.edu/ams-ml4es/home

• Amanda Burke, University of Oklahoma ([email protected])
• Benjamin Toms, Colorado State University ([email protected])
• Katherine Avery, University of Oklahoma ([email protected])
• Hamid Kamangir, Texas A&M Corpus Christi ([email protected])
• Karthik Kashinath, Lawrence Berkeley National Laboratory ([email protected])
• Ryan Lagerquist, University of Oklahoma ([email protected])

1. Introduction to Machine Learning and AI
2. Data Science Fundamentals
3. Supervised Learning Algorithms
4. Introduction to Deep Learning

1. Unsupervised Learning Overview
2. Machine Learning Model Interpretation

The modules for this short course require Python 3.6 and the following Python libraries:

• numpy
• scipy
• matplotlib
• xarray
• netcdf4
• pandas
• scikit-learn
• tensorflow-gpu or tensorflow
• keras
• jupyter
• ipython
• jupyterlab
• ipywidgets

The data for the course are stored online. The download_data.py script will download the data to the appropriate location and extract all files. The netCDF data is contained in a 2GB tar file, so make sure you have at least 4GB of storage available and a fast internet connection.

To run the notebooks on the cloud rather than a local installation, see the short course website Machine Learning in Python for Environmental Science.