Resources

Reference Textbooks

Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron [available online @ Brown Library and linked via E-Reserves in Canvas]
Elements of Statistical Learning: Data Mining, Inference and Prediction [pdf] by Hastie, Tibshirani and Friedman
Introduction to Data Science: Data Analysis and Prediction Algorithms with R [link] by Rafael Irizarry

Linear Algebra Review and Reference [pdf] by Zico Kolter and Chuong Do, for Stanford University's Machine Learning Course (CS229)
For additional linear algebra review materials, see pre-lecture materials for Week 2: Mathematical Foundations on the Lectures page.

Seeing Theory [interactive] [pdf] by Daniel Kunin, Jingru Guo, Tyler Dae Devlin, and Daniel Xiang

Python Programming and Numerical Methods: A Guide for Engineers and Scientists (Q. Kong, T. Siauw, A. Bayen / UC Berkeley)
Geo-Python Course (Department of Geosciences and Geography, University of Helsinki)
Intro to Earth Data Science (Earth Lab CU Boulder)
Plotting and Programming in Python (Software Carpentry)
An introduction to Python for scientific computing (M. Scott Shell / UCSB)
An Introduction to Numpy and Scipy (M. Scott Shell / UCSB)
Python and NumPy Tutorial (J. Johnson / Stanford Computer Science)

AI For Earth Sciences Workshop [ICLR 2020] [NeurIPS 2020]
LANL Machine Learning in Solid Earth Geosciences Conference [2019]
Machine Learning Advances Environmental Science Workshop [ICPR 2020]
Tackling Climate Change with Machine Learning Workshop [ICRL 2020] [ICML 2021]
International Conference on Climate Informatics [2020]
NOAA Workshop on Leveraging AI in Environmental Sciences [2020]