Clustering & Density Estimation

In course

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Description

This lecture covers the concepts of dimensionality reduction, focusing on Principal Component Analysis (PCA), and discusses the objectives and methods of PCA. It also introduces clustering techniques like K-means clustering and Mean Shift, along with density estimation methods such as Kernel Density Estimation and Gaussian Mixture Models.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_5g4f0geh

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Non-parametric statistics, Statistical hypothesis testing

Data collection: Statistical sampling

Information engineering

Data science: Topics in data science

Machine learning: Unsupervised learning

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