Nonlinear Supervised Learning

This lecture covers various nonlinear supervised learning methods, including the inductive bias of different methods, such as neural networks, convolutional neural networks, transfer learning, recurrent neural networks, tree-based methods, and support vector machines. It explores how sufficiently large neural networks can approximate any function, the challenges of hyper-parameter tuning, and the advantages of different nonlinear methods in finding a good fit for specific datasets.