**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.

Lecture# Linear Regression: Least Squares

Description

This lecture covers the concept of linear regression using the method of least squares to find the best-fitting line to a set of data points. It explains how to minimize the sum of squared errors and solve the normal equations. The lecture also discusses the conditions for unique solutions and the implications of linearly independent columns in the matrix A.

Official source

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.

In course

DEMO: esse adipisicing dolor anim

Eu anim ex aliqua elit sit pariatur reprehenderit in quis cupidatat veniam. Cupidatat duis eiusmod deserunt sint ut magna. Aliqua nulla nulla aute eiusmod officia ullamco id do officia elit aliqua.

Instructor

enim irure ex

Ad excepteur proident ipsum dolor et laboris veniam. Labore culpa occaecat voluptate cupidatat ea. Consectetur sint nisi deserunt sit eiusmod officia dolore id labore id. Est veniam consectetur enim aute magna proident fugiat consequat aute aute aliqua. Occaecat ut sit aliquip dolore minim officia.

Ontological neighbourhood

Related lectures (96)

Matrix Equivalence Theorems

Explores matrix equivalence theorems for systems of equations and least squares solutions.

Least Squares Solutions

Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.

Singular Value Decomposition: Applications and Interpretation

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Linear Algebra Basics

Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.

Linear Algebra: Applications and Matrices

Explores linear algebra concepts through examples and theorems, focusing on matrices and their operations.