DS 5050
Deep Learning in Environmental Science
Course Description
Equips students with some of the most used deep learning architectures. Explore feed-forward networks, convolutional neural networks, UNETs, encoders-decoders, generative adversarial networks and transformers. Analyze tools of explainable AI. Focused on climate applications, apply these techniques to real-world data, solving problems in prediction, pattern recognition, and data-driven insights.