Resources
Many of the links below are copied from Spring 2022 course website. Please contact the course instructional staff if any of the links are broken - we'll update them!
Reference Textbooks
Recommended
Introduction to Statistical Learning with Applications in Python [pdf] [website] by James, Witten, Hastie, Tibshirani and Taylor [Note: we will be using the 2023 edition in Python]
Additional references
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
Mathematical Foundations of Data Science
Linear Algebra
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.
Probability and Statistics
Seeing Theory [interactive] [pdf] by Daniel Kunin, Jingru Guo, Tyler Dae Devlin, and Daniel Xiang
Prismia Course Modules
Python Programming
Python tutorials
Core Python Language module in Earth and Environmental Data Science (R. Abernathey et al. / Columbia)
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)
Google Colab
What is Colaboratory? (Google)
Colab FAQs (Google)
How to Deal with Files in Google Colab neptune.ai)
[video] File Handling in Google Colab for Data Science (Data Professor)
ML + Earth community
Climate Change AI (newsletter)
ML + Climate Conferences and Workshops
AI For Earth and Space Sciences Workshop [ICLR 2020] [NeurIPS 2020] [ICLR 2022]
Machine Learning Advances Environmental Science Workshop [ICPR 2021]
Tackling Climate Change with Machine Learning Workshop [ICRL 2020] [ICML 2021] [NeurIPS 2022] [ICLR 2023]
International Conference on Climate Informatics [2020] [2022]
NOAA Workshop on Leveraging AI in Environmental Sciences [2020] [2022]
Machine Learning for Climate and Weather Applications Workshop [2022]