Lecture

Bayesian Statistics: Regularization and Divergence

In course

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Description

This lecture covers the concepts of Kullback-Leibler divergence, regularization, and Bayesian statistics. It explains how these techniques are used to combat overfitting in machine learning models, with a focus on the Bayesian view of assuming randomness in the data. Examples of logistic regression and probability calculations are provided.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_1zob1aqu

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Ontological neighbourhood

Statistics

Data analysis: Cross-validation, Regression analysis

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