Machine Learning, ETI, Universität Siegen, NRW » Machine Learning at ETI (University of Siegen, NRW)

The Department of Electrical Engineering and Computer Science (ETI) at the University of Siegen places a significant emphasis on machine learning research and education. The research focus at ETI centers around machine learning and visual computing, with multiple chairs dedicated to advancing the field through regular publication in premier machine learning conferences. In addition, in 2022, the University of Siegen was awarded one of the eight highly competitive German DFG research units in artificial intelligence. Within the Learning2Sense research unit, seven professors in Siegen collaborate on an interdisciplinary project combining machine learning and sensor technology.

Furthermore, ETI recognizes the importance of incorporating machine learning into its curriculum. The department offers a vast array of lectures, seminars, and project groups pertaining to machine learning for both computer science and electrical engineering students. Additionally, ETI is one of the first departments in Germany to make a machine learning introductory lecture mandatory for all computer science BSc students. This is in recognition of machine learning’s fundamental role in computer science alongside mathematics, theoretical computer science concepts, algorithms, and data structures, databases, and software engineering.

The following table lists the relevant chairs at ETI, their research focus, and their area of application in machine learning, as well as relevant lectures.

_ChairPerson(s)Research FocusArea of ApplicationTeaching
Analogue Circuits
and Image Sensors
Prof. Dr. Choubey
Image Sensors

Neural Networks in Analogue ICs


Near-sensor neural networks

Intelligent Imagers

Edge Processing
Neural Networks in
Circuits (part of ASME 1)
Computer GraphicsProf. Dr. Kolb
Hybrid, Data- and Model-
Driven Sensor Data Processing

Differentiable Sensor
Simulation and Rendering

Scene Reconstruction

Computational Imaging
Computer VisionProf. Dr. MöllerHybrid Machine Learning
and Energy Minimization

Inverse Imaging Problems

Computer Vision

Deep Learning

Recent Advances
in ML (43VSA0141V)

Projektgruppe Computer
Vision (43VSA0105V)

Intelligent SystemsProf. Dr. BeelAutomated Machine
Learning (AutoML)

Curriculum Learning

Few-Shot Learning
(Siamese Neural Networks)
Recommender Systems

Search Engines

Introduction to Machine
Learning (43ISG1188V)

Einführung in Complex and
Intelligent Software
Systems (43ISG1122V)

Recommender Systems

AutoML Project Group

Machine Learning
Competition Seminar

Prof. Dr. BlanzMedical Shape

Human Visual Perception

Machine Vision /
Maschinelles Sehen

Statistical Learning /
Statistische Lerntheorie

Prof. Dr. LochauMachine Learning in
Complex Software Systems

Machine Learning for
Software Configuration
and Software Quality

Quality Assurance of
ML/AI-based Software

Applications of ML in
Software Engineering
(in Softwaretechnik II,

Selected topics on ML/AI
in Software Engineering
and Software Quality
Assurance (Seminar Model-
Based Engineering, 43MBE1016V)

Ubiquitous Computing

Prof. Van LaerhovenEmbedded Learning
Activity Recognition

Wearable Sensing

Seminar Data Science
Visual ComputingProf. Dr. KeuperNeural Architecture

Graph Embeddings

Convolutional Neural

Deep Fake Detection

Robustness in Computer Vision

Motion Segmentation

Object Tracking

Unsupervised Deep
Learning (43LVC2104V)

Higher Level Computer
Vision (43LVC2202V)

Praktikum Maschinelles
Lernen (43LVC2105V)

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Aktualisiert um 11:32 am 19. January 2022 von Joeran Beel