VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
Statistical Learning for Atmospheric and Climate Science
Last Updated: 2026-06-03 00:07:58
Abstract
This course offers a systematic introduction to statistical and machine learning methods with focus on applications in atmospheric and climate science. Focus is on the theoretical and mathematical basis of supervised statistical learning (advanced regression, nonparametric methods) and their application in practice with hands-on exercises.
Objective
Students: - Understand the theoretical basis of machine learning - Are familiar with overarching concepts such as bias-variance trade-off, cost-functions, hyper parameters, cross-validation - Have good command of the theoretical basis of selected machine learning tools - Are able to select the appropriate statistical learning tools to tackle atmospheric and climate research problems - Can apply methods of statistical learning in atmospheric and climate research
Content
- Data in atmospheric and climate research (data types, observations, models) - Exploring properties of atmospheric and climate data (data in space and time, multivariate data) - Concepts of supervised learning (bias variance trade-off, overfitting, cross-validation) - Advanced linear regression (multiple linear regression, regularization) - Non-linear regression (local linear regression, regression trees, gradient boosting, random forests, neural networks) - Bootstrapping - Keynote speakers showcasing recent topics in statistical learning and high-level applications for atmospheric and climate research
Resources
Literature
Hastie, T., Tibshirani, R. & Friedman, J. (2009). The elements of statistical learning (Ed. 2). New York: Springer series in statistics. (Link to book: https://web.stanford.edu/~hastie/Papers/ESLII.pdf , book homepage: http://web.stanford.edu/~hastie/ElemStatLearn/ ) James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: springer. (Link to book: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf , book homepage (exercises, etc.): http://www-bcf.usc.edu/~gareth/ISL/ ) Bishop & Bishop (2023), Deep Learning Foundation and Concepts. Springer. (Link to book: https://doi.org/10.1007/978-3-031-45468-4 ). Book homepage: https://www.bishopbook.com/
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 30
- Signup Start
- 14.09.2026
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Statistical Learning for Atmospheric and Climate Science | No time listed | 2 h weekly |
Offered In
-
-
-
-
Deep Track Courses (At least 20 credits must be completed within the deep track courses. Surplus credit points can be counted towards the electives.)
-
-
Deep Track Planetary Science (These courses can be credited either as a specialization subject or as an elective subject.)
-
-
-