Gaussian Mixture Models: Data Classification

In course

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Description

This lecture covers the concept of denoising discrete valued signals using Gaussian mixture models. It explains how to model samples generated by different classes and the process of data classification. The instructor demonstrates the application of the EM algorithm for maximizing the likelihood function of Gaussian mixture models. The lecture also delves into the analysis of EMG signals for the classification of muscular pathologies and the segmentation of images using Markovian Gaussian mixture models.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_sy4gxofq

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing, Bayesian statistics

Data collection: Statistical sampling

Computer engineering

Computer data storage: File systems

Information engineering

Data science: Topics in data science

Artificial intelligence: Computer vision

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