Tag - Machine learning | ECMWF eLearning

Machine learning

Courses tagged with "Machine learning"

MLWC MOOC 3: Applications of Machine Learning in Weather and Climate

Six modules giving ML applications in observations, forecasting, data assimilation, post-processing, ocean and more.

Certification course: No

What you'll learn:

The application of ML methods to a range of problems in weather and climate
The details of practical code examples which can be used to apply to your own problems

Prerequisites:

Please complete MOOC MLWC - 2. Concepts of Machine Learning first, or ensure you are familiar with the topics covered there. Intermediate proficiency with Python, knowledge of statistics and experience in weather/climate is assumed.

Full description:

In this third tier of our MOOC on Machine Learning (ML) in Weather and Climate, we focus on the implementation of ML in weather and climate problems.

This six-module course gives code examples and explainers in topics including:

Satellite precipitation removal
Environmental modelling
Nowcasting
Data assimilation
Downscaling
Ocean modelling
Operational meteorology

This course is aimed at those with a technical/weather background and experience in Python. Ideally you will have already taken Tiers 1 and 2 of the MOOC on ML in Weather and Climate, or already be familiar with the concepts there.

Please note that this course ran live in early 2023 and reflected the state of the art at that point in time.

Estimated duration: 25 hours

Hide last updated info: No

Hide number of enrollments: No

Hide full description label: No

Atmospheric composition: No

Climate: Yes

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Machine Learning

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.

Level: Fundamentals

Certification course: No

What you'll learn:

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.

Prerequisites:

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.

Full description:

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.

Estimated duration: 14 hours

Hide last updated info: No

Hide number of enrollments: No

Hide full description label: No

Atmospheric composition: No

Climate: Yes

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Machine Learning

MLWC MOOC 1: Introduction to Machine Learning in Weather and Climate

Six modules introducing the main topics in machine learning in the context of weather and climate.

Level: Fundamentals

Certification course: No

What you'll learn:

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

Prerequisites:

A basic knowledge of weather and climate, statistics and computing.

Full description:

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.

Estimated duration: 14 hours

Hide last updated info: No

Hide number of enrollments: No

Hide full description label: No

Atmospheric composition: No

Climate: Yes

Computing services and tools: Yes

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Machine Learning