{"id":679,"date":"2021-05-26T20:27:37","date_gmt":"2021-05-26T18:27:37","guid":{"rendered":"https:\/\/wp.unil.ch\/dawn\/?page_id=679"},"modified":"2025-01-07T15:41:55","modified_gmt":"2025-01-07T14:41:55","slug":"getting-started-with-machine-learning","status":"publish","type":"page","link":"https:\/\/wp.unil.ch\/dawn\/getting-started-with-machine-learning\/","title":{"rendered":"Getting Started with Machine Learning"},"content":{"rendered":"\n

Below is a non-exhaustive list of open resources to help environmental scientists get started with machine learning; consider reaching out by email<\/a> if you think an open<\/em> resource is missing or outdated. <\/p>\n\n\n\n

Tutorials with Code<\/summary>\n
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Machine Learning Courses<\/strong><\/p>\n\n\n\n

Coursera Machine Learning<\/a>: Beginner courses in machine learning <\/p>\n\n\n\n

fast.ai<\/a>: Online deep learning course<\/p>\n\n\n\n

DeepLearning.AI<\/a>: Online deep learning course<\/p>\n\n\n\n

Stanford CS20<\/a>: Tensorflow\/Keras course<\/p>\n\n\n\n

Stanford CS231n<\/a>: Popular course in deep learning for computer vision<\/p>\n\n\n\n

paperswithcode.com<\/a>: Trending machine learning articles with code<\/p>\n\n\n\n

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Machine Learning for Environmental Science Courses<\/strong><\/p>\n\n\n\n

Machine Learning in Weather & Climate<\/a>: Massive Open Online Course designed by ECMWF<\/a>, competence.lu<\/a>, and various experts (including \u2202AWN<\/a>)<\/p>\n\n\n\n

Applied Machine Learning Tutorial for Earth Scientists<\/a>: Short hands-on course designed by the Barnes Research Group<\/a><\/p>\n\n\n\n

Machine Learning for Weather and Climate<\/a>: Short course designed by CIRA for atmospheric science researchers <\/p>\n\n\n\n

Explainable Artificial Intelligence for Environmental Science<\/a>: Short course designed for AI2ES\/CIRA researchers<\/p>\n\n\n\n

Artificial Intelligence for Earth Monitoring<\/a>: Hands-on course from the Copernicus Programme for students<\/p>\n\n\n\n

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To Get Started before Machine Learning<\/strong><\/p>\n\n\n\n

An Introduction to Earth and Environmental Data Science<\/a>: Basics of research computing in Earth science for students<\/p>\n\n\n\n

Pythia Foundations<\/a>: A community learning resource for Python-based computing in the geosciences <\/p>\n\n\n\n

Programming with Python<\/a> by \u00a9 Software Carpentry and \u00a9 Data Carpentry: To focus on the fundamentals at a relaxed pace<\/p>\n\n\n\n

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Useful Libraries for Machine Learning<\/strong><\/p>\n\n\n\n

Scikit-learn Tutorial<\/a>: Python library for scientific machine learning<\/p>\n\n\n\n

Tensorflow Tutorial<\/a>: Python library for deep learning<\/p>\n\n\n\n

PyTorch Tutorial<\/a>: Python library for deep learning <\/p>\n\n\n\n

MLib Tutorial<\/a>: Spark library for machine learning<\/p>\n\n\n\n

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Literature Reviews on Machine Learning for Environmental Science<\/summary>\n
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Eyring et al. (2024): Pushing the frontiers in climate modeling and analysis with machine learning<\/em><\/a><\/p>\n\n\n\n

Beucler et al. (2021): Machine Learning for Clouds and Climate<\/em><\/a><\/p>\n\n\n\n

Reichstein et al. (2019): Deep Learning and Process Understanding for Data-Driven Earth System Science<\/em><\/a><\/p>\n\n\n\n

Lai et al. (2025): Machine Learning for Climate Physics and Simulations<\/em><\/a><\/p>\n\n\n\n

Bergen et al. (2019): Machine Learning for Data-Driven Discovery in Solid Earth Geoscience<\/em><\/a><\/p>\n\n\n\n

Rampal et al. (2024): Enhancing Regional Climate Downscaling through Advances in Machine Learning<\/em><\/a><\/p>\n\n\n\n

Beucler et al. (2024): Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications<\/em><\/a><\/p>\n\n\n\n

Bracco et al. (2024): Machine learning for the physics of climate<\/a><\/p>\n\n\n\n

Eyring et al. (2024): AI-empowered next-generation multiscale climate modelling for mitigation and adaptation<\/em><\/a><\/p>\n\n\n\n

Rolnick et al. (2019): Tackling Climate Change with Machine Learning<\/em><\/a><\/p>\n\n\n\n

Willard et al. (2020): Integrating Physics-Based Modeling with Machine Learning: A Survey<\/em><\/a> <\/p>\n\n\n\n

Sonnewald et al. (2021): Bridging observations, theory and numerical simulation of the ocean using machine learning<\/em><\/a><\/p>\n\n\n\n

Huntingford et al. (2019): Machine learning and artificial intelligence to aid climate change research and preparedness<\/em><\/a><\/p>\n\n\n\n

Molina et al. (2023):<\/a> A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena<\/a><\/em><\/p>\n\n\n\n

Chantry et al. (2021): Machine Learning for Weather and Climate Modelling<\/em><\/a><\/p>\n\n\n\n

Irrgang et al. (2021): Towards neural Earth system modelling by integrating artificial intelligence in Earth system science<\/em><\/a><\/p>\n\n\n\n

de Burgh-Day and Leeuwenburg (2023): Machine Learning for numerical weather and climate modelling: a review<\/em><\/a><\/p>\n\n\n\n

Raghu and Schmidt (2020): A Survey of Deep Learning for Scientific Discovery<\/em><\/a> <\/p>\n<\/div><\/div>\n<\/details>\n\n\n\n

Textbooks and Pedagogical Articles<\/summary>\n
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G\u00e9ron (2019): Hands-on Machine Learning with Scikit-Learn, Keras & Tensorflow<\/em> [code<\/a>]\n\n\n\n

Chollet (2017): Deep Learning with Python<\/em> [code<\/a>]\n\n\n\n

Molnar (2021): Interpretable Machine Learning<\/em> [website<\/a>]\n\n\n\n

Goodfellow et al. (2016): Deep Learning<\/em> [website<\/a>] <\/p>\n\n\n\n

Foster (2019): Generative Deep Learning<\/em> [website<\/a>\/code<\/a>]\n\n\n\n

Nielsen (2019): Neural Networks and Deep Learning<\/em> [website<\/a>\/code<\/a>]\n\n\n\n

Parr and Howard (2018): The Matrix Calculus you Need for Deep Learning<\/em> [pdf<\/a>]\n\n\n\n

James et al. (2013): An Introduction to Statistical Learning<\/em> [website<\/a>\/pdf<\/a>\/code<\/a>]\n\n\n\n

Hastie et al. (2017): The Elements of Statistical Learning<\/em> [website<\/a>\/pdf<\/a>]\n\n\n\n

Bishop (2006): Pattern Recognition and Machine Learning<\/em> [pdf<\/a>\/code<\/a>\/Matlab<\/a>]\n<\/div><\/div>\n<\/details>\n\n\n\n

Environmental Datasets for Machine Learning Research<\/summary>\n
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List of benchmark datasets<\/a> (maintained by Pangeo<\/a>)<\/p>\n\n\n\n

Open datasets for machine learning<\/a> (maintained by Kaggle<\/a>) <\/p>\n<\/div><\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"

Below is a non-exhaustive list of open resources to help environmental scientists get started with machine learning; consider reaching out by email if you think an open resource is missing or outdated.<\/p>\n","protected":false},"author":1002254,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"templates\/template-full-width.php","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"class_list":["post-679","page","type-page","status-publish"],"_links":{"self":[{"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/pages\/679","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/users\/1002254"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/comments?post=679"}],"version-history":[{"count":5,"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/pages\/679\/revisions"}],"predecessor-version":[{"id":3443,"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/pages\/679\/revisions\/3443"}],"wp:attachment":[{"href":"https:\/\/wp.unil.ch\/dawn\/wp-json\/wp\/v2\/media?parent=679"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}