Machine Learning in Signal Processing

In machine learning, and especially in deep learning, the focus is on both fundamental and applied research. Machine learning algorithms are developed for anomaly detection, uncertainty estimation, out-of-distribution detection, few-shot learning, noisy label learning, as well as hardware motivated problems such as model compression and efficient design of neural network architectures. Applications range from automated driving, computer vision and medical image analysis to signal processing. For example, trajectory and motion prediction, localisation, and image and point cloud segmentation are some examples of practical problems.