Lecture

Dimensionality Reduction: PCA & t-SNE

In course

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Description

This lecture covers the concepts of Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction. PCA focuses on finding the directions of maximum variance in the data, while t-SNE aims to preserve the topology of the data in a low-dimensional space. The lecture explains the mathematical principles behind these methods and their applications in visualizing high-dimensional data.

Instructor

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Official source

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

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks, Topics in machine learning

Data science: Dimensionality reduction

Mathematics

Algebra: Linear algebra

Statistics

Statistical inference: Mathematical statistics

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