Teaching

Summer semester 2019.

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

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.

Winter semester 2018/19.

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

Winter semester 2018/19.

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

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.