Explores predictive models and trackers for autonomous vehicles, covering object detection, tracking challenges, neural network-based tracking, and 3D pedestrian localization.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Covers controlling behavior in animals and robots through oral presentations, computational exercises, and a mini-project for hands-on neuroscientific discovery.
