Fueled by the exponential increase in computational power and the vastly increasing amount of data, deep learning has emerged as a powerful, alternative approach to the traditional machine learning methods. Especially in the field of computer vision, strong progress has been made on automatic recognition of image content using deep Convolutional Neural Networks (CNNs). Tasks that were unimaginable only a decade ago, are now relatively easily implemented using such CNNs. This course introduces this end-to-end machine learning approach for the automatic interpretation of visual content: image classification, semantic segmentation, object detection and more. The course program addresses both the theoretical underpinnings and the practical implementation of convolutional neural networks, thereby offering an essential toolset for computer vision scientists in a wide variety of applications domains, ranging from medical imaging to surveillance.