Lecture

Regression Reloaded: Synthetic Dataset Construction and Parameter Estimation

In course

Pariatur amet duis cillum veniam magna elit ea ipsum Lorem occaecat enim et ea commodo. Ut ut qui ex incididunt magna velit eiusmod anim occaecat amet. Nostrud ex ut aliqua ut cupidatat cupidatat sunt voluptate culpa ipsum magna sunt. Veniam ad adipisicing non est do. Do minim culpa nisi fugiat dolore. Ex culpa est aliquip consequat sit do.

Description

This lecture covers the construction of a synthetic dataset for linear regression, the estimation of ground truth parameters using least squares, and the verification that the least square solution aligns with the maximum of the log-likelihood. It also explores L2 regularization as a prior for parameter estimation.

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

Instructor

non proident

Minim officia magna aliquip id aliqua officia. Velit id eiusmod nostrud eiusmod ad pariatur ullamco ut anim commodo pariatur do Lorem. Ullamco id id quis occaecat laboris consectetur cupidatat dolor elit est. Mollit enim id eiusmod ad.

Official source

https://mediaspace.epfl.ch/media/0_uwky8wgj

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

Related lectures (35)

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.