Teaching

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.

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 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

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

Winter semester 2020

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

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.