Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Untitled

Official source

https://switch-video-production.os.zhdk.cloud.switch.ch/transcoding/f0/4d/80a277ea4b06efe5e84293003662/h264-1080p.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=c70ce910fb73491f9933421af7a4fcee%2F20240725%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240725T142918Z&X-Amz-Expires=86400&X-Amz-SignedHeaders=host&X-Amz-Signature=e2e21962ff8a0b90d7e799cd71da83357013fda9c8f6f579513444fc3d4e3e04

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

Mathematics

Algebra: Linear algebra

Statistics

Data analysis: Regression analysis

Related concepts (32)

System of linear equations

In mathematics, a system of linear equations (or linear system) is a collection of one or more linear equations involving the same variables. For example, is a system of three equations in the three variables x, y, z. A solution to a linear system is an assignment of values to the variables such that all the equations are simultaneously satisfied. A solution to the system above is given by the ordered triple since it makes all three equations valid. The word "system" indicates that the equations should be considered collectively, rather than individually.

Least squares

The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each individual equation. The most important application is in data fitting.

Linear differential equation

In mathematics, a linear differential equation is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives, that is an equation of the form where a0(x), ..., an(x) and b(x) are arbitrary differentiable functions that do not need to be linear, and y′, ..., y(n) are the successive derivatives of an unknown function y of the variable x. Such an equation is an ordinary differential equation (ODE).

Residual sum of squares

In statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared estimate of errors (SSE), is the sum of the squares of residuals (deviations predicted from actual empirical values of data). It is a measure of the discrepancy between the data and an estimation model, such as a linear regression. A small RSS indicates a tight fit of the model to the data. It is used as an optimality criterion in parameter selection and model selection.

Lausanne

Lausanne (loʊˈzæn , USalsoloʊˈzɑːn , lɔzan; Losena lɔˈzəna) is the capital and largest city of the Swiss French-speaking canton of Vaud. It is a hilly city situated on the shores of Lake Geneva, about halfway between the Jura Mountains and the Alps, and facing the French town of Évian-les-Bains across the lake. Lausanne is located northeast of Geneva, the nearest major city. The municipality of Lausanne has a population of about 140,000, making it the fourth largest city in Switzerland after Basel, Geneva, and Zurich, with the entire agglomeration area having about 420,000 inhabitants (as of January 2019).

Related lectures (27)

Untitled

QR Factorization: Least Squares System Resolution MOOC: Linear Algebra (Part 3)

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

Linear Equations: Least Squares Solution MOOC: Linear Algebra (Part 3)

Explains how to solve linear equations using the least squares method to minimize errors in the system.

Linear Independence and Basis

Explains linear independence, basis, and matrix rank with examples and exercises.

Row-echelon and reduced row-echelon matrices MOOC: Warm-up for EPFL MOOC: Linear Algebra (Part 1)