mar366 - Current topics in modelling and data analysis

VL and SE Machine learning in the environmental sciences
The students acquire the latest methods in the field of mathematical modeling and analysis of large datasets (Big Data) and their application areas. They are capable of implementing analyses using the Matlab language. They learn to engage with current literature and critically evaluate the latest methods regarding data security and usability in a scientific context.

 

VL and SE Machine learning in the environmental sciences
In this course the students will learn to think as a data scientist and ask questions about the data. First, we will learn how to work with tables and extract statistics on groups of data. Then, we will go to the basic approaches of machine learning: supervised learning (classification and regression trees, neural networks), unsupervised learning (cluster analysis, factor analysis), reducing system dimensions (PCA, MDA ect.), statistical modelling (regression, generalized linear models), and optimization of model parameters (simulated annealing, differential evolution). Finally, we will focus on typical workflow of the data processing. We will use Matlab to implement the algorithms.

Präsentation oder Hausarbeit am Ende der Veranstaltungszeit nach Maßgabe der Dozentin oder des Dozenten.

1 benotete Prüfungsleistung

Präsentation oder Hausarbeit

Aktive Teilnahme
Aktive Teilnahme umfasst die Präsentation eines Themas in Form eines Seminarvortrags, wenn die Prüfungsleistung eine Hausarbeit ist, oder die schriftliche Ausarbeitung, wenn die Prüfungsleistung ein Seminarvortrag ist, sowie die Beteiligung an der Diskussion von Seminarbeiträgen.