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16.08.2022 20:51:54
mar768 - Statistical Analysis (Complete module description)
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Module label Statistical Analysis
Module code mar768
Credit points 6.0 KP
Workload 180 h
(
Kontaktzeit: 56 h, Selbststudium: 124 h
)
Institute directory Institute for Chemistry and Biology of the Marine Environment
Applicability of the module
  • Master's Programme Environmental Modelling (Master) > Mastermodule
Responsible persons
Freund, Jan (Module responsibility)
Peppler-Lisbach, Cord (Module counselling)
Ruckdeschel, Peter (Module counselling)
Ryabov, Alexey (Module counselling)
Strodthoff, Nils (Module counselling)
Prerequisites
keine
Skills to be acquired in this module

Die Studenten besitzen erweiterte Kenntnisse über Analyse- und Modellierungsmethoden von Umweltdaten.

Module contents

Spezielle Methoden der Statistischen /

Stochastischen Modellierung (VL, Ü, S)

Spezialvorlesung (teilweise mit Übung) oder Seminar mit wechselnden Inhalten, um aktuelle Forschungsgebiete der statistischen und stochastischen Modellierung darzustellen. Beispielhafte Inhalte: z.B. Korrelation, Kausalität und ihre Rekonstruktion aus multivariaten Zeitreihen, Generalisierte Regression, Mathematische Grundlagen der Angewandten Statistik, Computerintensive Verfahren.

 

Seminar Komplexe Systeme und Modellierung (S)

Heranführung an aktuelle Themen in der Umweltmodellierung

 

VL/Ü 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.

(only Sommersemester)

 

VL, Ü Practical Deep Learning in PyTorch

This lecture will provide a general introduction to modern deep learning methods with a particular emphasis on practical applicability. At the same time, the course will provide an introduction to the popular PyTorch Deep Learning framework while requiring only basic programming skills in Python. The course will cover a range of common machine learning tasks across different data modalities ranging from tabular data over Computer Vision (image classification, image segmentation) to time series and natural language processing. It will cover the most important model architectures in these domains ranging from convolutional neural networks over recurrent neural networks to transformers. The lecture will be accompanied by a tutorial class where students are supposed to acquire hands-on experience in working with PyTorch and are supposed to acquire the skills to apply Deep Learning methods in their respective fields of study.

Reader's advisory

Fachliteratur wird in der Lehrveranstaltung bekannt gegeben.

Links
Languages of instruction German, English
Duration (semesters) 1 Semester
Module frequency
Module capacity unlimited
Modullevel / module level MM (Mastermodul / Master module)
Modulart / typ of module Wahlpflicht / Elective
Lehr-/Lernform / Teaching/Learning method Winter- und Sommersemester
Auswahl von Veranstaltungen von insgesamt 6KP
SE Kolloquium: Komplexe Systeme und Modellierung (3 KP)
VL, Ü, SE Spezielle Methoden der Statistischen und Stochastischen Modellierung (3 KP oder 6 KP) (WP) (WiSe oder SoSe)
VL Machine learning in the environmental sciences
(2 SWS, 3 KP) (SoSe)
SE Machine learning in the environmental sciences
(2 SWS, 3 KP) (SoSe)
VL, Ü Practical Deep Learning in PyTorch (2+2 SWS, 3+3 KP) (WiSe)
Vorkenntnisse / Previous knowledge Erfahrung im Umgang mit R oder Matlab.

VL, Ü Practical Deep Learning in PyTorch
Erfahrung im Umgang mit Python
Course type Comment SWS Frequency Workload of compulsory attendance
Lecture
2 SuSe or WiSe 28
Seminar
1 SuSe or WiSe 14
Exercises
1 SuSe or WiSe 14
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module

Klausur am Ende der Veranstaltungszeit oder alle anderen möglichen Prüfungsleistungen nach Maßgabe der Dozentin oder des Dozenten

KL