Reinforcement Learning ConceptsCovers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Deep Learning FundamentalsIntroduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Deep Neural NetworksCovers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Perception: Data-Driven ApproachesExplores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Generalization in Deep LearningDelves into the trade-off between model complexity and risk, generalization bounds, and the dangers of overfitting complex function classes.