Unsupervised Learning: Clustering & Dimensionality Reduction

In course

Description

This lecture covers the fundamentals of unsupervised learning, focusing on clustering with K-means and dimensionality reduction using Principal Component Analysis (PCA). It explains how unsupervised learning identifies patterns in data without predefined labels. The instructor discusses the K-means algorithm for grouping data points based on proximity and PCA for finding a new set of features that best represent the data. The lecture also introduces autoencoders as neural networks for dimensionality reduction. Practical examples and applications are provided to illustrate the concepts.

Official source

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

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