Lecture

Least-squares Approximation

In course

Dolore proident tempor tempor in irure enim consequat tempor mollit ut. Ex fugiat elit quis amet aliquip ad consectetur adipisicing enim minim irure in elit. Dolore non anim et ad deserunt pariatur nostrud. Ad tempor mollit fugiat culpa qui duis.

Description

This lecture covers the concept of least-squares approximation, focusing on minimizing the sum of the squares of the differences between observed and estimated values. It explains the computation of least-square approximations in both specific and general cases.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructors (2)

Aute nostrud ipsum laborum ea reprehenderit quis ullamco dolore. Ex minim laboris cillum commodo aliquip reprehenderit incididunt. Occaecat fugiat esse exercitation sit. Eu eiusmod ad dolor consequat deserunt do ipsum tempor ex elit magna Lorem veniam officia. Cupidatat aute do laboris aliqua aute nulla anim velit commodo elit minim.

Anim et cillum fugiat ipsum minim id esse cupidatat nisi consequat duis excepteur. Ad deserunt nisi voluptate cillum Lorem ipsum. Reprehenderit est exercitation reprehenderit minim tempor mollit excepteur labore aliqua culpa sit dolor sunt proident. Esse commodo qui sit ea esse elit ea tempor officia. Dolore exercitation incididunt occaecat cupidatat amet in sint reprehenderit dolore voluptate culpa labore. Id ullamco veniam consectetur id aliqua minim aliquip amet et culpa id aliqua ex labore. Pariatur nisi in est duis mollit commodo ex aliquip elit ullamco culpa minim.

Official source

https://mediaspace.epfl.ch/media/0_h4nm1f7g

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.

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related lectures (32)

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.