Useful learning resources
Useful learning resources¶
Here are some great learning resources that I’ve found helpful.
Bold means highly recommended.
Atmospheric Science and Numerical Modelling¶
Art of Climate Modeling, Paul Ullrich, UC Davis.
Artificial Intelligence for Earth System Science (AI4ESS) Summer School, 2020, National Center for Atmospheric Research.
Artificial Intelligence (AI) for Earth Monitoring, EUMETSAT, ECMWF, MOi, EEA, Copernicus.
Engineering Mathematics, University of Washignton, Mechanical Engineering 564 and 565.
If you’re interested in jobs in this area, I highly recommend Workera to help figure out what the roles are, what you’re suited to, what you need to improve on, and personalised plans to make this progress.
Machine learning, Coursera, Andrew Ng.
Video lectures, CS229, Standford University.
Machine Learning for Intelligent Systems, Kilian Weinberger, 2018.
CS4780, Cornell: Video lectures.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, Aurélien Géron, 2019, O’Reilly Media, Inc.
NYU Deep Learning, Yann LeCun and Alfredo Canziani, NYU, 2021.
Physics-based Deep Learning, Nils Thuerey, Philipp Holl, Maximilian Mueller, Patrick Schnell, Felix Trost, Kiwon Um, 2021.
Artificial Intelligence: A Modern Approach, 4th edition, Stuart Russell and Peter Norvig, 2021, Pearson.
Artificial Intelligence: Principles and Techniques, Percy Liang and Dorsa Sadigh, CS221, Standord, 2019.
Linear Algebra, Gilbert Strang, MIT 18.06, 2005.
Essence of linear algebra, 3Blue1Brown.
Essence of calculus, 3Blue1Brown.
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Gilbert Strang, MIT 18.065, 2018.
Production Machine Learning Systems, Google Cloud, Coursera.
Machine Learning for Healthcare, MIT 6.S897, David Sontag and Peter Szolovits, 2019.
Computational and Inferential Thinking: The Foundations of Data Science, Ani Adhikari and John DeNero, Data 8: Foundations of Data Science course, UC Berkeley.
Python Data Science Handbook, Jake VanderPlas, 2016.
Trustworthy Online Controlled Experiments : A Practical Guide to A/B Testing, Ron Kohavi, Diane Tang, and Ya Xu.
Causal Diagrams: Draw Your Assumptions Before Your Conclusions, Miguel Hernan, Harvard University.
Introduction to Causal Inference, Brady Neal.
If you’re interested in jobs in this area, I highly recommend Teach Yourself Computer Science by Oz Nova and Myles Byrne. This will help navigate the key topics and best resources. Many of the resources below are directly taken from this great guide.
Algorithms and Data Structures:
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, O’Reilly Media, Inc., 2017.
Distributed Systems, MIT 6.824, Robert Morris, 2020.
High Performance Computing:
High Performance Computing Course - Advanced Scientific Computing, Prof Ing Morris Riedel, University of Iceland.
Research Software Engineering:
Research Software Engineering with Python, The Alan Turing Institute.