After successful completion of the course, students are able to develop and apply deep learning methods for automatic image analysis (e.g. for classification of images or detection of people in images).
Deep learning for automatic image analysis:
* Brief recap of Computer Vision and Image Processing
* Machine Learning: overview, parametric models, iterative optimization
* Feedforward Neural Networks, backpropagation
* Convolutional Neural Networks for classification, detection, and segmentation
* Generative models for image synthesis
* Deep Learning for 3D and unstructured data
* Software libraries and practical aspects
* Preprocessing, data augmentation, regularization, visualizations
* Algorithmic Governance, Trustworthy AI and ethical Aspects
The contents presented in the lecture will be applied in exercises.
The student has to be enrolled for at least one of the studies listed below
