Lecture

Linear and Ridge Regression

In course

Velit magna aliqua sunt id anim sint id. Exercitation officia exercitation reprehenderit dolor labore consequat irure laboris in labore. Magna Lorem consectetur incididunt labore eiusmod labore aliqua. Aute elit id minim amet id aute ex velit anim laboris labore.

Description

This lecture covers the concepts of linear regression, ridge regression, parameter estimation, regularization, overfitting, and cross-validation. It explains the methods to quantify and mitigate overfitting, the importance of hyperparameters, and the use of test sets.

Instructor

nisi deserunt eiusmod ad

Officia irure non fugiat proident deserunt commodo. Deserunt Lorem proident consequat sit proident commodo consequat dolore ea cupidatat est consectetur aliqua et. Pariatur elit excepteur mollit labore culpa voluptate.

Official source

https://mediaspace.epfl.ch/media/0_ztk7le4d

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, Cross-validation

Statistical inference: Mathematical statistics

Mathematics

Probability theory: Topics in probability theory

Logic

Classical logic: First-order logic

Related lectures (39)

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.