Course Description
This course will equip students with some of the most commonly used deep learning architectures. We will explore feed-forward networks, convolutional neural networks, UNETs, encoders-decoders, generative adversarial networks and transformers. We will also analyze tools of explainable AI. Focused on environmental applications, students will apply these techniques to real-world data, solving problems in prediction, pattern recognition, and data-driven insights. Solid background in probability, statistics, and in coding (preferably Python) is recommended for enrollment in this course.
