Nonlinear Supervised Learning

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Description

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.

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https://mediaspace.epfl.ch/media/0_hho7atur

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