Explores deep learning for autonomous vehicles, covering perception, action, and social forecasting in the context of sensor technologies and ethical considerations.
Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Explores socially-aware AI for last-mile mobility, focusing on understanding social etiquettes, anticipating behaviors, and forecasting crowd movements.
Explores trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios.
