AI for Earth and Sustainability Science | AI for Good Discovery
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14 learners
What you'll learn
This course includes
- 47.3 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 37 lessons • 47.3 hours of video
AI for Earth and Sustainability Science | AI for Good Discovery
37 lessons
• 47.3 hours
AI for Earth and Sustainability Science | AI for Good Discovery
37 lessons
• 47.3 hours
- Distributional robustness for climate change 01:04:13
- Risks and solutions when using non-stationary training data in earth system science 59:19
- Do AI models know what they know? How explainable AI can help us trust what machines learn 01:05:00
- AI for humanity: Using earth observation for solving global challenges 59:57
- When rivers meet neural nets: The promise and limits of Machine Learning in hydrology 01:00:36
- Forecasting and understanding bird migration with process-guided deep learning 56:04
- AI for early warnings addressing floods and droughts 55:57
- Remote Sensing and Machine Learning for Environmental Monitoring: Opportunities and Challenges 01:08:04
- Adapting machine learning for atmosphere-biosphere coupling in earth system models 01:12:22
- Earth Observation & Climate Research through Self-Supervised Learning 01:19:23
- Causality in Dynamical Systems 01:11:51
- How deep learning improves weather forecasts and reduces energy demands 01:09:56
- Toward robust surrogate components of land surface states for hybrid earth system models 01:00:00
- Deep learning for large-scale ecosystem monitoring 01:30:10
- Harnessing Machine Learning and satellite data for planetary-Scale Impact 01:30:03
- Tracking permafrost landscape dynamics in a rapidly warming Arctic 01:19:34
- Deep Generative Models for Molecular Simulation 01:14:59
- Fast statistical inference with neural networks and amortisation: Golden ticket or red herring? 01:16:49
- Large-scale monitoring of nature with deep learning 01:16:41
- Confronting Climate Change with Generative and self-supervised Machine Learning 01:29:39
- Physics-constrained machine learning for scientific computing 01:15:01
- Explain it to me! On the use of explainable ML for the agricultural and environmental sciences 01:00:36
- Building fast emulators in climate modeling 01:15:06
- Airborne light fields for wildlife observation 01:11:20
- Using AI for sustainable agriculture and forecasting 01:16:01
- Using machine learning to track human development at global scale and high spatial resolution 01:14:56
- Food security and AI: From data collection to early warning systems 59:56
- Machine learning for Earth Observation: Emissions, mitigation and sequestration 01:16:10
- Model assessment & interpretation in Geospatial ML: spatial dependence & high dimensionality 01:10:43
- Long Short-Term Memory networks for large-scale rainfall-runoff modeling | Earth & Sustainability 01:30:21
- The role of AI in achieving the Sustainable Development Goals 01:29:16
- AI for flood forecasting | Grey Nearing at Google 01:29:51
- Climate modeling with AI: Hype or Reality? & Deep learning and the dynamics of physical processes 01:59:45
- Towards physics-AI hybrid modeling in hydrology: Opportunities and challenges 01:32:21
- Mapping urban forest across North America: Computer vision for large-scale environmental monitoring 01:29:53
- Remote sensing enables monitoring life above and under water | Earth & Sustainability Science 02:00:56
- Physics-informed ML to push the ocean frontier in climate | Maike Sonnewald, Princeton University 01:30:46
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