Dimensionality Reduction

In course

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Description

This lecture covers the basics of artificial neural networks, including activation functions, multilayer perceptrons, training methods, and working with images. It then delves into dimensionality reduction techniques such as Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Kernel PCA, and t-distributed Stochastic Neighbor Embedding (t-SNE). The instructor explains the intuition behind each method, their mathematical formulations, and provides examples to illustrate their applications.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_vvdb8nce

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

Data science: Dimensionality reduction

Statistics

Statistical inference: Mathematical statistics

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