MOOC MLWC - 2. Concepts of Machine Learning
The second tier of our MOOC on Machine Learning in Weather and Climate includes five modules and dives into the key concepts of Machine Learning, covering regression and 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.
MOOC MLWC - 1. ML in Weather & Climate
The first tier of our MOOC in Machine Learning in Weather and Climate includes six modules and is an introduction to the main topics in machine learning in the context of weather and climate, from the processing of observations to data assimilation, forecasting and post-processing.
- An overview of machine learning in weather and climate
- Unpack, at a conceptual level, key concepts and topics in ML
- Applications and recent advances in the field
A basic knowledge of weather and climate, statistics and computing.
In this first tier of our MOOC on Machine Learning (ML) in Weather and Climate, we give a broad overview of the key concepts ML and its applications in recent years to topics from forecasting and data assimilation to post-processing, observations and computing. This course is aimed as an introduction, which is accessible to those with an interest in ML, weather and climate, but without necessarily requiring a very technical background.
This course features a mixture of interactive modules, webinar recordings, quizzes, podcasts and code examples.
Please note that this course ran live in early 2023 and reflected the state of the art at that point in time.