Universität Wien

280356 VU Fundamentals of Machine Learning in Meteorology (2025S)

5.00 ECTS (3.00 SWS), SPL 28 - Erdwissenschaften, Meteorologie - Geophysik und
Continuous assessment of course work
Moodle
Th 26.06. 16:45-18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

The lecture takes place from 4:45 PM to 6:15 PM in German and from 6:15 PM to 7:45 PM in English. The language of the exercise sessions is based on the language preference of the students (German or English).

Friday: UZA II, 2G542, on 4 afternoons from 1:00 PM to 3:30 PM (3 x 45 minutes)

  • Thursday 06.03. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Thursday 13.03. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Thursday 20.03. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Thursday 27.03. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Thursday 03.04. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Friday 04.04. 13:00 - 17:00 Ort in u:find Details
  • Thursday 10.04. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Thursday 08.05. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Friday 09.05. 13:00 - 17:00 Ort in u:find Details
  • Thursday 15.05. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Thursday 22.05. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Friday 23.05. 13:00 - 17:00 Ort in u:find Details
  • Thursday 05.06. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Thursday 12.06. 16:45 - 18:15 Praktikumsraum Meteorologie 2F513 5.OG UZA II
  • Friday 13.06. 13:00 - 17:00 Ort in u:find Details

Information

Aims, contents and method of the course

Objectives:

To understand and apply machine learning methods in the field of meteorology.

Contents:

Overview of machine learning methods
Concepts of Machine Learning
Regressions and classifications
Clustering and dimension reduction
Decision trees
Artificial Neural Networks

Teaching via lecture (Powerpoint) and computer exercises

Assessment and permitted materials

Multiple Choice Test for Lecture Topics in last Lecture Unit (evaluation weight: 40 %)
One Test with Programming Tasks at end of last exercise block (evaluation weight: 30 %)
A presentation on a meteorological machine learning topic from the literature or the implementation and presentation of your own project (evaluation weight: 30%)

In multiple-choice tests no tools are allowed, in computer tasks any tools are allowed

Minimum requirements and assessment criteria

Multiple Choice Test for Lecture Topics in last Lecture Unit (Proportion of total evaluation: 40 %)
One Test with Programming Tasks at end of last exercise block (Proportion of total evaluation: 30 %)
One Presentation on a meteorological machine learning topic from literature (Proportion of total evaluation: 30 %)

Points of the individual partial performances are weighted, added up, standardized to the maximum points to be achieved and converted into a grade with the following key:

Score Key: 0-49.99%: 5, 50.00-62.49%: 4, 62.50-74.99%: 3, 75:00-87.49%: 2, > 87.50%: 1

Examination topics

Lecture content will be tested via multiple choice test

Additionally, students must give a presentation on a meteorological AI application from the literature or develop their own AI application using meteorological data.

The material from the exercises must be applied in the form of Python computer exercises.

Reading list

The Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani, Jerome Friedman)
An Introduction to statistical Learning (Gareth James, Daniela Witten, Robert Tibshirani, Jerome Friedman)
Python Machine Learning (Sebastian Raschka, Vahid Mirjalili)
Python Machine Learning by Example (Yuxi Hayden Liu)
Deep Learning for The Earth Sciences (Gustau Camps-Valls )

Association in the course directory

WM-AdvComMet

Last modified: Mo 17.02.2025 18:46