Teaching

Summer semester 2023

Seminar on the basics and application of diffusion models for image generation leading up to Stable Diffusion and beyond.

Winter semester 2023/2024

Seminar on the applications of computer vision algorithms on satellite imagery.

Winter semester 2023/2024

Seminar on the applications of computer vision algorithms in neuroscience.

Winter semester 2023/2024

Block seminar on current trends in segmentation.

Winter semester 2022

Seminar on the applications of computer vision algorithms on satellite imagery.

Winter semester 2021

Advanced architectures for vision, siamese neural networks, autoencoder, VAE, Bayesian Deep Learning, generative models, GANs, visualization techniques, transformers, semi-supervised and active learning.

Summer semester 2022

Machine Learning Basics, Linear Classifier, Multi-Layer Perceptron, Regularization, Training, Optimization, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks.

Summer semester 2022

One and two-stage detectors, semantic segmentation networks, single/multi-object tracking, message passing networks, trajectory prediction, 3D tracking.

Summer semester 2021

Seminar on recent trends in Automated Machine Learning.

Winter semester 2021

One and two-stage detectors, semantic segmentation networks, single/multi-object tracking, message passing networks, trajectory prediction, 3D tracking.

Winter semester 2020

Machine Learning Basics, Linear Classifier, Multi-Layer Perceptron, Regularization, Training, Optimization, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks.

Winter semester 2020

Sorting, searching, hash tables, graphs and weighted graphs.

Summer semester 2021

One and two-stage detectors, semantic segmentation networks, single/multi-object tracking, message passing networks, trajectory prediction, 3D tracking.

Summer semester 2020

One and two-stage detectors, semantic segmentation networks, single/multi-object tracking, message passing networks, trajectory prediction, 3D tracking.

Summer semester 2020

Advanced architectures for vision, siamese neural networks, autoencoder, VAE, attention mechanisms, graph neural networks, multi-dimensional CNN, generative models, GANs, visualization techniques.

Summer semester 2020

Machine Learning Basics, Linear Classifier, Multi-Layer Perceptron, Regularization, Training, Optimization, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks.

Winter semester 2019

Advanced architectures for vision, siamese neural networks, autoencoder, VAE, Bayesian Deep Learning, multi-dimensional CNN, generative models, GANs, visualization techniques.

Winter semester 2019

One and two-stage detectors, semantic segmentation networks, single/multi-object tracking, message passing networks, trajectory prediction, 3D tracking.

Winter semester 2020-21

Advanced architectures for vision, siamese neural networks, autoencoder, VAE, Bayesian Deep Learning, multi-dimensional CNN, generative models, GANs, visualization techniques.

Summer semester 2019

Advanced architectures for vision, siamese neural networks, autoencoder, VAE, Bayesian Deep Learning, multi-dimensional CNN, generative models, GANs, visualization techniques.

Summer semester 2019

Machine Learning Basics, Linear Classifier, Multi-Layer Perceptron, Regularization, Training, Optimization, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks.

Summer semester 2019

Winter semester 2018/19

Advanced architectures for vision, siamese neural networks, autoencoder, VAE, Bayesian Deep Learning, multi-dimensional CNN, generative models, GANs, visualization techniques.

Winter semester 2018/19

Machine Learning Basics, Linear Classifier, Multi-Layer Perceptron, Regularization, Training, Optimization, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks.

Summer semester 2018

Machine Learning Basics, Linear Classifier, Multi-Layer Perceptron, Regularization, Training, Optimization, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks.

Winter semester 2017/18

Machine Learning Basics, Linear Classifier, Multi-Layer Perceptron, Regularization, Training, Optimization, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks, Advanced architectures, Generative Models, Variational Autoencoders.