Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Provides an overview of policy gradient methods in reinforcement learning, focusing on the log-likelihood trick and the transition from batch to online learning.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
