MLWC MOOC 2: Concepts of Machine Learning in Weather and Climate
Five modules covering decision trees, deep learning, uncertainty and generative models, and physics-guided approaches.
- The main concepts of data retrieval using CliMetLab
- Theory and application of regression and decision trees
- Theory underlying deep neural networks, and software tools to apply them to practical problems
- Uncertainty quantification using Bayesian and generative approaches
- Physics-constrained approaches
By the end of this course, participants should have a sound understanding of the theory underpinning key ML approaches, and how these can be applied in practice.
Please complete MOOC MLWC - 1. ML in Weather & Climate first, or ensure you are familiar with the topics covered there. Basic proficiency with Python, knowledge of statistics and experience in weather/climate is assumed.
In this second tier of our MOOC on Machine Learning (ML) in Weather and Climate, we go deeper into the theory underpinning ML, and the tools that are used to implement it, using Python case studies in weather and climate.
This five-module course covers regression trees and random forests, explores workflows in ML, and dives deeper into the ingredients of deep neural networks, including architectures, training, tuning and evaluating models. We will also explore approaches to quantify uncertainty, including generative ML models and Bayesian neural networks. The last module covers physics-guided ML approaches with applications to post-processing.
This course is aimed at those with a technical/weather background, some experience in Python, and a solid understanding of statistics. Ideally you will have already taken Tier 1 of this MOOC or already be familiar with the concepts there.
This course features a mixture of interactive modules, webinar recordings, quizzes, podcasts, and contains a number of detailed code examples.
Please note that this course ran live in early 2023 and reflected the state of the art at that point in time.