Lecture

Unsupervised Learning: PCA & K-means

Description

This lecture covers unsupervised learning techniques such as Principal Component Analysis (PCA) for dimensionality reduction and K-means for clustering data. It explains how PCA creates new features as a linear combination of original features and how K-means groups data points into clusters. The lecture also touches on other dimensionality reduction techniques like Linear Discriminant Analysis (LDA) and Generalized Discriminant Analysis (GDA). Practical tips for PCA implementation and the formulation of optimization problems are discussed, along with the importance of covariance matrices in data analysis.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.