- January 28, 2024 at 8:00 AM - 3:45 PM Eastern Time (Hybrid)
- Baltimore Convention Center
- AMS Course Page
This AMS Short Course is an introductory course for researchers interested in learning about how methods from machine learning and data science can be applied to environmental research questions. This course will allow participants to interact with real-world data and develop ML pipelines in Python using Jupyter notebooks. This course will have a beginner section followed by an intermediate section. The beginner section will provide an introduction to machine learning and will cover topics such as supervised and unsupervised machine learning, with an introduction to deep learning. Participants will be guided through example code and notebooks to try out machine learning methods for themselves. Participants will become familiar with the ML pipeline, starting with an investigation of the dataset and its features. Then, participants will learn how to configure and train models for tabular and image-based datasets. Finally, participants will learn techniques for evaluating and comparing models to select the one that fits their needs. The intermediate section will assume that the participant is familiar with the basic ML pipeline and has some experience with model development. Topics will include physics-informed and Transformer-based architectures. Then, techniques to investigate what a trained model learned will be demonstrated using eXplainable AI (XAI) techniques.
| Time | Topic | Instructor |
|---|---|---|
| 8:00 - 8:30 | Introduction to AI for Environmental Science | Kara Lamb |
| 8:30 - 9:30 | Data Preprocessing & Exploring | Evan Krell |
| 9:30 - 9:45 | Coffee Break | |
| 9:45 - 11:15 | Learning Methods | Maria Molina |
| 11:15 - 12:00 | Model Evaluation | Hamid Kamangir |
| 12:00 - 1:00 | Lunch | |
| 1:00 - 1:45 | Physics-informed AI | Kara Lamb |
| 1:45 - 2:30 | Explainable AI (Part 1, Part 2) | Evan Krell |
| 2:30 - 2:45 | Coffee Break | |
| 2:45 - 3:30 | Transformers | Hamid Kamangir |
| 3:30 - 3:45 | Conclusions/Additional Resources |