VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.

651-4938-00L 2 Credits BSC , MSC , GS D-ERDW
You're viewing possible stale or outdated data. Please check the latest semester for more up-to-date information.

Machine Learning for Earth and Planetary Sciences III

Completion or parallel enrollment in Machine Learning for Earth and Planetary Sciences I and/or Machine Learning for Earth and Planetary Sciences II is required. Additionally, you will need to find a supervisor who provides a problem and data set and who accompanies you throughout the class.
VVZ CR n/a

Last Updated: 2026-02-05 16:38:32

Abstract

In this class you tackle an individual research problem by applying the machine learning methods you learned in Machine Learning for Earth and Planetary Science I and/or Machine Learning for Earth and Planetary Science II.

Objective

For a geoscience problem of your choice, you identify the most suitable machine learning technique(s), and apply them to the problem. You optimise and fine tune and evaluate the model, in order to find the best possible performance for the problem at hand. From this experience, you learn the intricacies and the power of a particular data science method, and to solve a data driven problem in detail.

Content

After a kick-off meeting you work independently with your supervisor, and present your project with a poster or powerpoint presentation at the end of the class. You can ask questions and get advice at weekly office hours.

General Information

Language
English
Levels
BSC , MSC , GS
Frequency
Yearly recurring

Examination

Type
graded semester performance
Graded project report

Course Components

Type Title Time & Place Hours
independent project Machine Learning for Earth and Planetary Sciences III No time listed 60 h semesterly

Offered In