Lecture

Generalization Error

In course

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Description

This lecture covers the concept of generalization error in machine learning, explaining how it relates to the distribution of data and the hypothesis used. It discusses the population risk, empirical risk, and learning algorithms, providing insights into the bounds on generalization error. The instructor also delves into the setup and problem statement, emphasizing the importance of understanding the data and the hypothesis to minimize errors.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_avvl9l6j

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Mathematics

Algebra: Linear algebra

Mathematical logic: Set theory

Information engineering

Machine learning: Artificial neural networks

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