Lecture

Generalization Bound

In course

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Description

This lecture covers the concept of generalization bound in machine learning, focusing on the relationship between empirical risk minimization and generalization error. It discusses the importance of simple hypothesis spaces, the role of distribution, and the fixed hypothesis. The instructor explains the theorems related to generalization and provides examples to illustrate the theoretical concepts.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_r75ovw2w

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

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