Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Explores the evolution of image representation, challenges in supervised learning, benefits of self-supervised learning, and recent advancements in SSL.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Covers model-free prediction methods in reinforcement learning, focusing on Monte Carlo and Temporal Differences for estimating value functions without transition dynamics knowledge.
Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
