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* 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.
Lecture and individual programming tasks in groups of two.
ECTS breakdown: 3 ECTS = 75h
16h lecture34h programming exercises24h exam preparations1h exam---75h
Written exam (50%) and compulsory programming exercises (50%).
The student has to be enrolled for at least one of the studies listed below
