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651-4908-00L 2 Credits MSC D-ERDW
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Machine Learning for Earth and Planetary Sciences II

Lecturers & Examiners: Prof. Dr. Cara Magnabosco
Machine Learning for Earth and Planetary Sciences II focuses on unsupervised and probabilistic learning and does not require enrollment in Machine Learning for Earth and Planetary Sciences I (supervised learning focused) or Machine Learning for Earth and Planetary Sciences III (project focused).
VVZ CR n/a

Last Updated: 2026-02-05 16:38:08

Abstract

This class provides an introduction to machine learning concepts, techniques and algorithms and their applications in Earth and Planetary Sciences. The course will cover both the fundamentals and application of machine learning techniques for research. Emphasis will be placed on unsupervised and probabilistic methods and their use in Earth and Planetary Sciences.

Objective

Students will learn the fundamentals of machine learning, data handling, data visualization and data analysis using python. A variety of unsupervised and probabilistic learning techniques will be introduced and applied throughout this course. In completing the course, students will learn how to: - Generate hypotheses from data. - Make predictions from data. - Evaluate a model’s performance. - Apply machine learning techniques to their own research.

Content

Machine Learning for Earth and Planetary Sciences II begins on Week 5 of the spring semester and consists of 2 hours of interactive lectures per week. Exercises focused on data handling, implementation of machine learning methods and analysis are assigned each week and culminate with a course-wide data analysis competition. This course will cover a variety of machine learning topics used by Earth and Planetary scientists, including: - Unsupervised learning (e.g. k-means clustering, PCA and t-SNE) - Semi-supervised learning - Ensemble learning methods (e.g. Random Forest) - Hidden Markov Models - Bayesian Inference - Applications of machine learning and artificial intelligence in Earth and Planetary Sciences Machine Learning for Earth and Planetary Sciences I is not required for enrollment in this course. Pre-requisites include Mathematics I - IV or equivalent; Some experience with programming in either python, R, or MATLAB

General Information

Language
English
Levels
MSC
Frequency
Every two years

Examination

Type
graded semester performance

Registration & Places

Max Places
25

Course Components

Type Title Time & Place Hours
lecture Machine Learning for Earth and Planetary Sciences II
Course starts in week 5 (22.3.2024)
  • Fri 10:15-12:00 (NO E 51.1)
18 h semesterly

Offered In

  • Earth Sciences Master
    • Electives (Courses can be chosen from the complete offerings of the ETH Zurich and University of Zurich (according to prior agreement with the MSc Committee).)