Lecture

Unsupervised Learning: Clustering and Dimension Reduction

In course

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Description

This lecture introduces unsupervised learning, focusing on clustering and dimension reduction. It covers the formalization of unsupervised learning, the search for relevant data partitions, and the concept of dimension reduction. Examples include the MNIST dataset and Isomap for nonlinear dimensionality reduction.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_5r3z28o6

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Ontological neighbourhood

Information engineering

Machine learning: Unsupervised learning, Artificial neural networks

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